professional summary in resume for data scientist

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Data scientist resume summary examples.

Approved by hiring managers, here are proven resume summary examples you can use on your Data Scientist resume. Learn what real hiring managers want to see on your resume, and when to use which.

• Data Scientist
• Data Analyst (Aspiring Data Scientist)
• Data Science Consultant
• Data Science Intern
• Data Science Manager
• Data Scientist (E-commerce Industry)
• Data Scientist (Healthcare Industry)
• Data Scientist (Transitioning from Software Engineering)
• Junior Data Scientist
• Senior Data Scientist
• Data Scientist resume templates
• Similar summary examples

Data Scientist Resume Summary Example

Showcasing use of relevant skills.

Firstly, stating the ability to use machine learning algorithms underlines your expertise in a critical area for a Data Scientist. It directly tells hiring managers that you have the know-how in one of the most sought-after skills in the industry.

Quantifying impact

The number 20% gives a tangible image of the applicant's impact across different companies. It's not just about stating that you've helped decrease churn rate, but showing how significant that change was. It's a good example of how you can make your achievements more meaningful.

Data Analyst (Aspiring Data Scientist) Resume Summary Example

Show your contribution to business growth.

Talking about how your statistical analysis led to a tangible business result (10% increase in ROI) shows that you understand the business side of data analysis. It also makes your accomplishment easy to grasp for non-technical hiring managers.

Demonstrate Your Role in Machine Learning Projects

By stating that you assisted in developing a machine learning model, you're subtly showing that you have experience with machine learning, which is a highly sought-after skill in data science roles. Your claim of improving sales forecasting accuracy by 12% makes it clear that you can contribute in a big way.

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Data Science Consultant Resume Summary Example

Proving impact on clients' businesses.

By mentioning the positive impact you've had on multiple clients, you show that you can adapt your skills to different contexts and still deliver valuable results. This is important for a consultancy role, where variety and adaptability are key.

Showing technical expertise

Designing a data warehousing solution for a multinational corporation is a strong example of technical proficiency. This shows potential employers that you are capable of handling complex, large-scale projects and can deliver technical solutions that streamline data management.

Data Science Intern Resume Summary Example

Point out contributions to key projects.

Directly linking your work to a project's success is a powerful way to show your value. By mentioning that you contributed to a project by using data mining techniques which in turn boosted the conversion rate, you showcase your practical skills and their real-world impacts.

Show Your Role in Reducing Errors

Mentioning your assistance in reducing false positives in an anomaly detection system highlights your technical abilities but also shows that you understand the importance of accuracy in the data science field. This paints a picture of you as a careful and precise professional.

Data Science Manager Resume Summary Example

Expressing leadership at scale.

Highlighting the experience in leading large teams signifies strong leadership skills and the ability to handle significant responsibilities. It also implies an understanding of managing resources and coordinating projects on a larger scale, which is particularly important for managerial roles.

Delivery of high-impact projects

Having managed the deployment of a system that boosted sales significantly showcases the applicant's ability to lead projects with significant financial impact. It implies that their work is not just technically strong, but also aligned with business goals.

Data Scientist (E-commerce Industry) Resume Summary Example

Quantify your success.

Success is more impressive when it's quantified. By mentioning that your personalization algorithm resulted in a 15% increase in the average order value for an online retailer, recruiters can easily see and understand the value you bring to the table.

Highlight Your Impact on Marketing

Mentioning how your customer segmentation model improved targeting for email marketing campaigns and boosted open rate by 20% shows your understanding of how data can directly impact marketing efforts. It also shows your ability to apply data science in practical and impactful ways.

Data Scientist (Healthcare Industry) Resume Summary Example

Showcase industry and role specificity.

By stating that you're a 'Data Scientist focused on the healthcare industry', you're immediately showing the recruiter that you have a specialized skill - you're not just a generic data scientist. Highlighting your industry of expertise right away gives you a competitive edge especially if the job you're applying for is within the same industry.

Highlight Impacts on Efficiency

By showing how your data visualization tool enhanced data accessibility leading to improved patient care efficiency by 15%, you're not just stating your achievement, but also the impact it had. It not only shows your technical skills but also your understanding of the end-user, in this case, the medical staff.

Data Scientist (Transitioning from Software Engineering) Resume Summary Example

Demonstrating transferable skills.

By mentioning the development of a machine learning module that increased user engagement, you show that you can leverage your previous experience in software engineering in your new role. This also shows your understanding of user behavior and how to use data science to improve it.

Automating processes

Reducing errors by automating data cleaning processes showcases your ability to leverage your programming skills to improve data quality. This is a crucial aspect of data science and shows employers you can identify and solve essential problems in the data pipeline.

Junior Data Scientist Resume Summary Example

Emphasizing contribution to specific tasks.

For a junior role, specifying the tasks you've contributed to is a good way to highlight your involvement in important data science processes. This indicates your hands-on experience and your potential to contribute to similar tasks in the future.

Assisting in impactful projects

Assisting in the development of a model that had a tangible, positive impact on customer attrition illustrates that you can play a role in important projects. It also highlights your understanding of customer behavior and the business side of data science.

Senior Data Scientist Resume Summary Example

Highlighting groundbreaking work.

By mentioning the pioneering use of AI systems, the applicant demonstrates initiative and a capacity for innovation. This is crucial for a senior role, where new approaches and creative problem-solving are often expected.

Demonstrating leadership

Leading a cross-functional team of 10 implies that the applicant has solid leadership skills and experience in managing diverse teams. This not only shows ability to coordinate, but also the capability to collaborate effectively with different specialists.

Data Scientist Resume Templates

Data mining.

Director of Analytics

Solutions Architect

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Data Scientist Resume - Sample & Guide for 2024

You’re a data scientist. You solve complex problems.

Your newest problem: writing a resume for that elusive data scientist role.

Fortunately, you’ve arrived at the best place. This guide will take you through a range of steps, so you can create a data scientist resume that gets results. 

• An example of a finished data scientist resume that works
• How to write a data scientist resume that’ll fill up your interview diary
• How to make your data scientist resume stand out [with top tips & tricks]

Before we get stuck into the data, here’s a data scientist resume example, created with our very own online resume builder :

This resume performs as well as it looks. Just follow the steps in this guide to create a data scientist resume that gets great results, just like the above example.

Besides our data scientist resume example, we've got even more resume examples for professionals in the computer science field:

• Data Analyst Resume
• Data Entry Resume
• Computer Science Resume
• Artificial Intelligence Engineer Resume
• Engineering Resume
• Software Engineer Resume
• Web Developer Resume
• Java Developer Resume

How to Format a Data Scientist Resume

Before you can reveal why you’re the best person for the job, you need to pick the best format.

Now, this is more important than it sounds.

It will allow your best attributes to ‘jump off the page’ into the recruiters' vision. 

The most common resume format is “ reverse-chronological ”, and it’s for good reason. Essentially, it allows the recruiter to immediately see the value that you provide. We recommend the majority of individuals start with this format.

The following resume formats also get our approval:

• Functional Resume – If you have strong skills, but a weak work history, then this resume format is recommended. It’s ideal for skilled scientists that don’t have a lot of experience or have gaps in their employment history
• Combination Resume – Acting as a combination of both the “Functional” and “Reverse-Chronological” formats, you can use a combination resume if you have a wealth of work experience

Once you’ve chosen your format, you need to organize your resume layout .

Use a Data Scientist Resume Template

As a data scientist, you present data in a structured way.

The same needs to happen to your resume.

However, creating a structured file isn’t an easy task!

You could use Word, but then you will have to risk the layout falling apart with every small alternation. 

Want to skip formatting issues? Use a data scientist resume template .

What to Include in a Data Scientist Resume

The main sections in a data scientist resume are:

• Work Experience
• Contact Information

Want to go a step further? You can also add these optional sections:

• Awards & Certification

Interests & Hobbies

What should you write for each section? 

Read on to learn how.

Want to know more about resume sections? View our guide on What to Put on a Resume .

How to Correctly Display your Contact Information

Now, there is no need to get creative in this section. 

The only requirement is accuracy. 

An incorrect contact section may mean the recruiter can’t contact you – disaster! 

The contact information section on your resume must include:

• Title – In this case, “Data Scientist”
• Phone Number – Check this multiple times for errors
• Email Address – Use a professional email address ([email protected]), not your childhood email ([email protected]).
• (Optional) Location - Applying for a job abroad? Mention your location.
• Ellie Branning, Data Scientist. 101-358-6095. [email protected]
• Ellie Branning, Data Scientist Whizz. 101-358-6095. [email protected]

How to Write a Data Scientist Resume Summary or Objective

It’s safe to say that recruiter’s don’t have time to dig into the data of every resume.

Instead, they scan the resume for the main points.

In fact, studies have shown that recruiters spend just a few seconds on each resume! 

So, what can you do?

You need an introduction that makes your value ‘jump off the page’.

To do this, use a resume summary or objective .

These are snappy paragraphs that go on top of your resume, just under your contact information. 

Now, this section is extremely important. This small paragraph could be the deciding factor between scoring an interview and simply having your resume dismissed.

But what is the difference between the two sections?

A resume summary is a 2-4 sentence summary of your professional experiences and achievements.

Certified data scientist with 12 years of experience for a diverse clientele. Achievements include updating data streaming processes for an 18% reduction in redundancy, as well as improving the accuracy of predicted prices by 18%. Highly-skilled in data visualization, machine learning, leadership.

A resume objective is a 2-4 sentence snapshot of what you want to achieve professionally.

Motivated data scientist with 2+ years of experience as a freelance data scientist. Passionate about building models that fix problems. Relevant skills include machine learning, problem solving, programming, and creative thinking.

So, which one is best, summary or objective?

Generally, we recommend that experienced data scientists go with a resume summary. Those who are new to the field, like graduates and career changers, would be better suited to an objective. 

How to Make Your Data Scientist Work Experience Stand Out

Recruiters need to be confident that you will do a good job for the company.

Listing your work experience is the easiest and best way to do this.

Here’s the best way to structure your work experience section:

• Position name
• Company Name
• Responsibilities & Achievements

Data Scientist

03/2016 - 05/2019

• Improved the accuracy of predicted prices by 18%.
• Coordinated a team of 16 data scientists working on 4 different projects.
• Updated data streaming processes for a 18% reduction in redundancy.

To separate your resume from the other applicants, you should talk about your best achievements, not your daily tasks. Doing so will clearly show how you can benefit the company.

Instead of saying:

“Data streaming.”

“Updated data streaming processes for an 18% reduction in redundancy.”

As you can see, the first statement doesn’t effectively convey your achievements. It shows that you streamed data, but it doesn’t show the results of your work. 

The second statement shows that you managed to reduce the redundancy numbers. Hard numbers that prove your skills – can’t argue with that!

What if You Don’t Have Work Experience?

Maybe you’re trying to break into the data science field?

Or maybe, you have already worked in the industry, but never in this specific role?

Your experience is null .

A recruiter will want data scientists that they can rely on. Whether you have job experience or not, being able to show that you have the skills is the most important factor.

If you already have proof of your data science skills, feel free to link to them in your resume.

With that said, there is still time to create a portfolio.

Here are several ways you can show your talents (and even get paid for it):

• Start freelancing.
• Offer your skills to friends and family.
• Contribute to open source projects on GitHub.
• If the above doesn’t work, become your own client! Show your skills by creating mock projects.

Are you recent data scientist graduate? Make sure to check out our student resume guide !

Use Action Words to Make Your Data Scientist Resume POP!

…are all common words that the recruiter sees time and time again.

However, you want to separate your resume from the competition, which means using power words to make your achievements stand out:

• Conceptualized
• Spearheaded

How to Correctly List your Education

Every great resume needs an education section.

But don’t worry, there is nothing too complicated here.

Simply enter your education history in the follow format:

• Degree Type & Major
• University Name
• Years Studied
• GPA, Honours, Courses, and anything else you might want to add

BSc in Statistics

University of Bath

2012 - 2016

• Relevant Courses: Probability and Statistics, Generalised Linear Models, Applied Statistics

Now, you may have some questions on this section. If so, here are the answers to some of the most frequent questions that we get:

• What if I haven’t finished education yet?

Regardless of whether you’re a data science graduate or still studying, you should mention all years studied to date

• Should I include my high school education?

The general rule is to only include your highest form of education. So, include your high school education if you don’t have a relevant degree for data science

• What do I put first, my education or experience?

Experiences are the priority, so those go first. If you’re a recent graduate, you will likely need to start with education.

Need to know more? Check out our guide on how to list education on a resume .

Top 15 Skills for a Data Scientist Resume

When it comes to the skills section, the hiring manager has seen it all before.

In fact, they need a data scientist to help with the entire pile of data scientist resumes!

You see, everyone lists all of their skills, even those that related to the job.

Your skill section should highlight your top skills in a way that is specific to the role.

Here are some of the most common data scientist skills:

Hard Skills for a Data Scientist Resume:

• Data Analysis
• Data Visualization
• Quantitative Analysis
• Machine Learning
• Mathematics
• Probability
• Programming

Soft Skills for a Data Scientist Resume:

• Critical Thinking
• Communication
• Time-Management
• Collaboration
• Data scientists frequently use tools, such as Cloudera, PERL, and OpenRefine. If there are any tools or pieces of software that you’re an expert in, include them in your skills section.

Here’s a more comprehensive list of 101+ must-have skills this year .

What Else Can You Include in a Data Scientist Resume?

We’ve now covered every essential resume section .

Is it the absolute BEST it can be?

Doing a great job with the above sections should be enough to get you shortlisted, but adding a few of the following sections could be the major factor in whether you become their new data scientist or not.

Awards & Certifications

Have you won an award for your work in a field that relates to data science?

Have you completed any courses to improve your skills and knowledge?

If you said yes to any of the above, make sure to mention them in your resume!

Don’t worry if you don’t have any awards or certificates, there a few companies that allow users to do online certifications, like Google.

• “IBM Data Science” - Coursera Certificate
• Google Certified Professional Data Engineer – GCP
• Microsoft Professional Program Certificate in Data Science
• “Deep Learning” - Coursera Certificate
• “Critical Thinking Masterclass” - MadeUpUniversity

Even though it is very unlikely to need a second language, you may want to add a small languages section to your resume. 

You see, being able to speak a second language is always an impressive skill to a hiring manager. 

Rank the languages by proficiency:

• Intermediate

Now, you may be wondering, “why would a recruiter need to know about my love for kayaking?”

Well, your hobbies reveal more about who you are as a person.

A hobbies section is an easy way to add personality to your resume, so add one if you have the space.

Here’s which hobbies & interests you may want to mention.

Include a Cover Letter with Your Resume

Here the thing –

Cover letters still play an important role during the application process.

They provide a number of benefits, but the main reason for using a cover letter is to show the recruiter that you care about working for their company.

To create a winning cover letter, we must use the correct structure. 

Here’s what we recommend:

You should complete the following sections:

Personal Contact Information

Your full name, profession, email, phone number, location, and website (or Behance / Dribble).

Hiring Manager’s Contact Information

Full name, position, location, email.

Opening Paragraph

It’s no secret that hiring managers skim through resumes and cover letters. As such, you need to hook the reader within the first few sentences. Use concise language to mention:

• The position you’re applying for
• Your experience summary and best achievement to date

Once you’ve sparked the reader’s interest, you can get deeper into the following specifics:

• Why you chose this specific company
• What you already know about the company
• How your skills relevant for the role
• Which similar industries or positions have you worked in before

Closing Paragraph

Don’t just end the conversation abruptly, you should:

• Conclude the points made in the body paragraph
• Thank the hiring manager for the opportunity
• Finish with a call to action. This is a good way to start a conversation. A simple “At your earliest opportunity, I’d love to discuss more about how I can help company X” will work

Formal Salutations

End the cover letter in a professional manner. Something like “Kind regards” or “Sincerely” will be proficient.

For more inspiration, read our step-by-step guide on how to write a cover letter .

Key Takeaways

If you followed all of the above advice, you’ve given yourself the best possible chance of landing that data scientist role.

Let’s quickly summarize what we’ve learnt:

• Format your data scientist resume correctly by prioritizing the reverse-chronological format and then following the content layout guidelines
• Start your resume with a summary or objective to hook the recruiter
• In your work experience section, give attention to your best achievements, rather than your responsibilities
• Craft a convincing cover letter for an unbeatable application

Suggested Reading:

• How to Ace Interviews with the STAR Method [9+ Examples]
• 22+ Strengths and Weaknesses for Job Interviews
• What Is Your Greatest Accomplishment? [3 Proven Answers]

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18 Data Scientist Resume Examples for 2024

• Data Scientist Resume
• Data Scientist Resumes by Experience
• Data Scientist Resumes by Role

Writing Your Data Scientist Resume

We’ve reviewed countless data scientist resumes and have made a concerted effort to distill what works and what doesn’t about each of them.

Our number one tip to create an effective data science resume is to quantify your impact on the business ! These 18 data scientist resume samples below and our  data scientist cover letter templates  can help you build a great job application in 2024, no matter your career stage.

Whether you’re looking for your first job as an entry-level data scientist or are a veteran with 10+ years of expertise, you’ll find plenty of tools to build your perfect resume, like our new  Word resume examples  or  free Google Docs resume templates .

Data Scientist Resume Example

or download as PDF

Why this resume works

• You need to  write your resume  in a way that  shows the employer that you’ve materially impacted the companies you’ve worked for.
• This means you should quantify your value in terms of business impact, not model performance. Model performance metrics without context really don’t convey much.
• They’re a way to quickly display your achievements and convince the employer that you’ll bring that same kind of energy to their team or company.

Data Science Student Resume

• For a splendid data science student resume, demonstrate a diverse skill set, prioritizing in-demand options (think Python, Jupyter Notebook, Pandas, Excel, SQL Server, etc.). Soft skills , ranging from teamwork and leadership to problem-solving, creativity, and adaptability are a welcome addition to your piece.

Entry-Level Data Scientist Resume

• Considering adding projects to your  entry-level data scientist resume  in lieu of enough work experience?
• You can demo the punch of a project by framing a question and then answering that question with data.
• Again, your results should be consistently expressed in numbers. Even if the result is as silly as saving 12 minutes per movie, it recognizes the importance of measuring impact.
• Customizing looks like: mentioning the target business by name and including relevant keywords from the  job description . 

Associate Data Scientist Resume

• When you have little to no professional background,  the skills you list on your resume  matter more than ever. And your abilities aren’t just selling points—they’re also a springboard for you to demonstrate your willingness to learn. 
• While writing your associate data scientist resume objective, immediately dive into any education or internship highlights with notable companies like Northrop Grumman. Then, sprinkle in some personality that shows your enthusiasm for new knowledge—drive and inquisitiveness are highly desirable traits in new professionals.

Senior Data Scientist Resume

• Your  senior data scientist resume  can really wow when you show a clear career progression from data analyst to data scientist to senior data scientist.
• That said, if you’ve got at least four years of experience under your belt, it’s fine for your work experience to account for about 70 percent of the page.
• A worthwhile summary should give a quick snapshot of your career highlights in two to three power-packed sentences and include the target company by name.

Data Scientist Intern Resume

• Call attention to your expertise in computer science by listing your proficiency in advanced programs like Keras on your data scientist intern resume.

Data Visualization Resume

• Whether it’s geospatial analysis, real-time data monitoring, or even creating standard visuals, make sure to quantify the impact of each and clearly state the benefit these tasks brought to the company to strengthen your data visualization resume.

Healthcare Data Scientist Resume

• Having two qualifications! Now’s the time to show all the degrees you’ve got! The best-case scenario is to have two degrees where one caters to the healthcare field while the other highlights your expertise in data science!

Amazon Data Science Resume

• Let that statement capture your aspirations and what you desire to bring to your new employer. Hiring managers are eager to see your passionate side and value to the team.

Python Data Scientist Resume

• Mentioning achievements such as improving project outcomes and reduction in process duration in your Python data scientist resume is a great way to leverage your experience honed over years of hard work.
• Then, by writing a great cover letter , you give yourself room to expound on exactly how you reduced process duration as a Python data scientist.

Data Scientist Machine Learning Resume

• Even if you already have ample experience in your field, you can give your data scientist machine learning resume a competitive edge by bringing your higher education to light. Create space to showcase your advanced degree in a relevant subject like statistics to further stand out.

Data Science Manager Resume

• Again, the results of your work should be stated clearly in terms of tangible impact (are you sensing a theme?). 
• Using a two-column layout for your  data science manager resume  allows more information to fit on a single page. Even with nine-plus years of experience, keeping your resume to one page is ideal.
• Fretting these details? Our  resume templates for 2024  may suit your specific needs; additionally, we’ve got fresh and  free Google Docs resume templates  that can make your  resume-creation  blues go away!

NLP Data Scientist Resume

• When you’re trying to figure out  what to put on your resume  for a more specialized role like an NLP data scientist, it’s important you showcase your proficiency in operationalizing models to have a big impact on the business.
• Don’t focus on the technical aspects of the models you’ve built on your  NLP data scientist resume  (you’ll talk more about that in your interviews). Instead, take a step back and talk about the broad impact you’ve had in your previous roles.

Metadata Scientist Resume

• Prove your experience in programming, testing, modeling, and data visualization through well-designed projects that solve real problems through code.
• The key isn’t to reinvent the wheel but to create something dynamic and unique that isn’t easily replicated with a few Google searches and a video tutorial.
• Solve this problem with projects. If you’ve worked on excellent projects that used and showcased the necessary skills required for the job, list them and watch your resume bloom with confidence!

Educational Data Scientist Resume

• Think “well-rounded” as you write; you might include an exciting publication related to the job role, quickly outline your relevant experience or abilities, and conclude with how and why you’ll better the company through your new role. 
• Skills and certifications add credibility, but potential employers also want to know about your impact.
• If you performed evaluations, what improvements did you make afterward? If you integrated machine learning, what optimizations did you use it for?

Data Analytics Scientist Resume

• Your data scientist, analytics resume should target the list of requirements that companies in your state commonly request.
• For example, 18 out of 20  job descriptions  for data science, analytics in the state of California list Python, SQL, R, Tableau, and Hadoop (in that order) as required skills.
• After you add job-market-specific data, our  free resume checker  can assess your resume for other key elements like spelling, grammar, and active language. 

Data Science Consultant Resume

• To best represent your capabilities, use metrics to talk about your accomplishments.

Data Science Director Resume

• For an effective data science director resume, use a clean and simple resume template and format your work experience in reverse-chronological order. Doing so will put your most recent and relevant accomplishments at the top, making it the first thing a recruiter will look at.

Related resume guides

• Data Analyst
• Data Engineer
• Computer Science

Recruiters only spend an  average of seven-plus seconds reviewing your resume , so it’s vitally important that you catch their attention in that time. Our guide for 2024 takes you section by section through your resume to ensure you get that first interview.

You can successfully choose a winning  resume format in 2024  that will snag an employer’s attention.

Short on time? Here are the quick-hit summaries of each section you can apply to your resume:

• Whether for a company or yourself, what you’ve worked on should be the focus of your resume. Always try to include a measurable impact of your work.
• Make this the job title you’re looking for (e.g., “data scientist”), and don’t worry about a summary unless you’re making a career change.
• Only include technical skills that you’d be comfortable having to code with/in during an interview. Avoid a laundry list of different skills.
• Include relevant courses if you’re looking for an entry-level role. Otherwise, make your work the focus of your resume. If you went to a boot camp, list it here.
• Double-check everything. This is not the place you want to make a mistake. You don’t need to put your exact address. City, state, and zip are fine.
• Try to keep it to one page. Keep your bullets brief. Triple-check your grammar and spelling, and then have someone else read it.
• Read the  data scientist job description . See if any projects you’ve worked on come to mind while reading it. Incorporate those specific projects into your resume.

Your data science projects and work experience

Let’s jump right into the good stuff and talk about the most important part of your resume: your work experience and projects. This is it. This is the grand finale. This is where the person reviewing your resume decides whether or not you’ll get an interview.

When talking about your previous work (whether that’s for another employer or on a side project), your goal is to convince the person reviewing your resume that you’ll provide value to their company. This is not the place to be humble. We want to see that “I’m wearing my favorite outfit” level of confidence.

The template for successfully talking about your experience as a data scientist is:

• Clearly state the goal of the project
• You can mention the programming languages you used, the libraries, modeling techniques, data sources, etc.
• State the quantitative results of your project

You’re a data scientist, so highlight your value by demonstrating the quantitative impact of your work.  These can be estimates . For example, did you automate a report? Roughly how many hours of manual work did you save each month? Here are some ideas for how you can quantitatively talk about your projects:

Ways to define the impact of your data science work

• Example:  You developed a pricing algorithm that resulted in a $200k lift in annual revenue.
• Example:  You built a model to predict who would cancel their subscription and introduced an intervention to improve monthly retention from 90% to 93%.
• Example:  You built a marketing attribution model that helped the company focus on marketing channels that were working, resulting in 2,100 more users.
• Example:  You ran an experiment across different product features, which resulted in a 25% increase in engagement rate.
• Example:  As a side project, you built a movie recommendation engine that now saves you 26 minutes each time you need to decide which movie to watch.
• Example:  Since you built a customer segmentation model to determine how to communicate with different customer types, customer satisfaction is up 17%.

Numbers draw attention, are convincing, and make your resume more readable. Which of these two ways to describe reporting is more compelling?

• Used Python, SQL, and Tableau to conduct daily reporting for the business
• Using Python, SQL, and Tableau, combined 11 data sources into a comprehensive, real-time report that saved 10 hours of work weekly

If nothing else, please take this away from this guide:  state the results of your projects on your resume in numbers.

Trade-offs between projects and work experience

Simply put, the more work experience you have, the less space “projects” should take up as a section on your resume. In the sample resumes above, you’ll notice that only the more entry-level data scientist resumes have a section for projects.

The senior-level resumes focus on projects in the context of experience within companies. Real estate is precious on a one-page resume, so you’ll want to focus on the bullets that most clearly demonstrate how you’re a great fit for the job. Companies want to hire data scientists who have demonstrated success at other companies.

Entry-level data science projects for resume

Junior data scientists should include projects on their resumes. Try starting with a  resume outline , where you can brain dump anything and everything about your projects; then, you can distill the best of it into your final resume. Can you share the Github link? Do you have a link to a write-up you did about your project?

The more initiative you can show for entry-level data science projects, the better. Do you have any questions to which you’ve always wanted the answer? You can probably think of some clever ways to get data around that question and come up with a reasonable answer. For example, our co-founder wanted to know  which data science job boards were best , so he pulled together some data, laid out his assumptions and methodology, and made his conclusions.

No matter what projects you include on your resume, be sure to clearly state the question you were answering, the tools and technologies you used, the data you used to answer the question, and the quantitative outcome of the project. Succinctly stating conclusions and recommendations from your analysis is a highly sought-after skill by employers in data science.

The data scientist summary

Since you have limited space on your resume, you should only include a  resume objective  if you take the time to customize it for each role to which you apply.

You may want to include a  resume summary  or objective when you’re making a big career change. If you do include one, make sure to keep it specific about your goal and experience. This is valuable space you’re going to be using on this statement, so take the time to personalize it to each job.

Include the title of the job you’re looking for under your name. This should be aspirational. So if you’re a data analyst looking to apply for data scientist jobs, you would put “data scientist” under your name as the headline:

Skills that pay the bills

The most common mistake we see on data science resumes (that we used to make on our resumes) is what we call skill vomit. It’s a laundry list of skills in which no one person could have expertise. A quick rule of thumb:  if the skills section takes up a third of the page, it takes too much space. This is a big red flag for hiring managers.

The reason people make such an exhaustive skills section is to get through the mythical data science resume keyword filters. If you’re changing your resume in small ways for each job you apply to (for example, put Python for jobs that mention Python and R for jobs that list R if you know both), you’ll have no problem with those keyword filters.

The rule of thumb that we recommend you use in determining whether to include a skill on your resume is this:  i f it’s on your resume, you should be comfortable coding with/in it during an interview.

So that means if you’ve read a few articles on Spark or adversarial learning, but you can’t use them in code, they should not be on your resume. If you only have a handful of tools under your toolbelt, but you can use them effectively to answer questions with data, you’ll be able to find jobs looking for that skill set. 

We can assure you there are all kinds of data science jobs available. Our scraper that indexes jobs across thousands of company websites shows over 5,000+ full-time data science job openings in the US across all tenures and skill sets. And our scraper has a lot of room for improvement, so that’s significantly lower than the actual number. 

There are tons of fish in the job market sea; you just need a fishing rod.

Entry-level vs. senior skills sections

Generally, the more senior you are, the shorter your skills section needs to be. If you’re a senior data scientist, you should talk about the major tools and languages you use but save specific modeling techniques for the “Work Experience” section. Show how you used particular models in the context of your work.

When you’re more junior, you likely haven’t had the chance to use all of the techniques you’re comfortable with within work or a project. That’s okay! It’s expected. But you still want to make it clear to a potential employer that you can use those methods or libraries.

Education is a lot like skills in that the more senior you are as a data scientist, the less space the education section should take up on your resume. When you’re looking for one of your first data science jobs, you might want to include courses relative to data science to demonstrate you have a strong foundation.

Classes in subjects like linear algebra, calculus, probability, and statistics and any programming classes are directly relevant to being a data scientist. If you’re looking for your first job out of college, you should include your GPA on your resume. When you have a few years of work experience, it’s not necessary to include it.

If you just finished (or are finishing) a data science boot camp, this is the place to list where you went. You can include the relevant lessons or classes you took. Be sure to have a few projects from your boot camp (especially if it was an original project) in your resume’s “Projects” section.

Contact information

The takeaway from this section is simple:  this is not where you should make a mistake . Storytime! When our co-founder was first applying to jobs out of college, he realized about 20 applications in, he had spelled his name “Stepen” instead of “Stephen.” Don’t pull a Stepen.

Data suggests that when your email is wrong, your response rate from companies drops to zero percent. That’s just math. We’ve seen exactly four data science resumes where the email address on the resume was incorrect.

Make sure your email address is appropriate. While we don’t doubt the authenticity of your “ [email protected] ” email, maybe don’t use it when applying for jobs. To play it safe, stick to a combination of your name and numbers for your email.

This is the section you can include anything you want to show off for a data science role. Have a blog where you document the analysis you do for Dungeons & Dragons? Active on Github or an open-source project? Include a link to anything relevant to data that will help you stand out in your application.

General resume formatting tips

This section is just a list of one-off styling and formatting tips for your data science resume:

• Keep it brief. Bullets should be informative but should not drag on for paragraphs.
• Each bullet point in your resume should be a complete thought. You don’t have to have periods at the end of each bullet.
• Keep your tense consistent. If you’re referring to old projects in the past tense, do that for all old projects.
• Please, please don’t get your contact information wrong.
• Don’t give the person reviewing your resume a silly reason to put it in the “No” pile.  Check your resume  carefully.

Customization for each application

You don’t have to go overboard with your resume customization. Here are the steps we recommend to customize it for each job:

• So in this example, we’ll have one “Python” resume and one “R” resume depending on what the job is seeking.
• For example, if you have experience with attribution modeling and this is a marketing data science role, you should include that experience.
• Do you have experience with a certain library or modeling technique they mention? 
• Do you have experience in the domain of the specific job?
• Do you have any relevant industry experience with the company?

Let’s walk through a specific example to highlight what we mean by including particular projects for different jobs. Let’s say that a senior data scientist is applying for the position below.

In the “Ideally, you’d have” section, they mention they want someone who has “Experience with ETL tools.” Let’s say that in reality, the candidate had a large role in building out data pipelines in his fictional role as a senior data scientist at EdTech Company.

So all we’d do is change that section of his experience at EdTech Company to talk about that project, as you see below:

Data science resume customization example

Original bullet on the resume: Worked closely with the product team to build a production recommendation engine in Python that improved the average length on the page for users and resulted in $325k in incremental annual revenue

Customized for the role: Built out our company’s ETL pipeline with Airflow, which scaled to handle millions of concurrent users with robust alerting/ monitoring

Customization for startups

For early-stage startups (anything less than 50 employees), one of the most important qualities they’re looking for in a hire is ownership. That means they want someone who can ask a question and come up with an answer with minimal instruction. 

If you want to stand out to these companies, you should demonstrate ownership in the way you list projects on your resume. Include active words like “drove” or “built” instead of passive language like “worked on” or “collaborated on.” We know this seems nit-picky, but this matters to early-stage companies. Hiring managers at companies this size are strained for time and will use any signal to weed people out.

Concluding thoughts

There you have it—a compelling, easy-to-read data science resume built for 2024. Now you can celebrate by doing something as fun as  writing a resume . Maybe your taxes? Or go to the dentist?

By building or  updating your current resume , you took a huge step toward landing your next (or first) data science job. Now please, we beg you, check your grammar and spelling again and have someone else read your resume. Don’t let that be the reason you don’t get an interview.

Congrats! The first and hardest step is done. You have a data science resume! With great power comes great responsibility, so go and apply wisely.

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Data Scientist Resume Examples For 2024 (20+ Skills & Templates)

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Looking to score a job as a Data Scientist?

You're going to need an awesome resume. This guide is your one-stop-shop for writing a job-winning Data Scientist resume using our proven strategies, skills, templates, and examples.

All of the content in this guide is based on data from coaching thousands of job seekers (just like you!) who went on to land offers at the world's best companies.

If you want to maximize your chances of landing that Data Scientist role, I recommend reading this piece from top to bottom. But if you're just looking for something specific, here's what's included in this guide:

• What To Know About Writing A Job-Winning Data Scientist Resume
• The Best Skills To Include On A Data Scientist Resume

How To Write A Job-Winning Data Scientist Resume Summary

How to write offer-winning data scientist resume bullets.

• 3 Data Scientist Resume Examples

The 8 Best Data Scientist Resume Templates

Here's the step-by-step breakdown:

Data Scientist Resume Overview: What To Know To Write A Resume That Wins More Job Offers

What do companies look for when they're hiring a Data Scientist?

Companies look for candidates with strong technical skills in programming languages like Python or R and experience with data manipulation, statistical analysis, and machine learning models. Companies are also looking for data scientists with problem-solving skills who can obtain actionable insights from complete datasets.

Your resume should show the company that your personality and your experience encompass all these things.

Additionally, there are a few best practices you want to follow to write a job-winning Data Scientist resume:

• Tailor your resume to the job description you are applying for: Tailor your resume for each application, aligning your skills with the specific requirements of each job description.
• Detail previous experiences: Provide detailed descriptions of your roles, emphasizing hard and soft skills related to the job description.
• Bring in your key achievements: Showcase measurable achievements in previous roles and share your best work.
• Highlight your skills:   Highlight your skills in Sales, Marketing, Communication, Customer Experience, and Management.
• Make it visually appealing: Use a professional and clean layout with bullet points for easy readability. Also, ensure formatting and font consistency throughout the resume and limit it to one or two pages.
• Use keywords: Incorporate industry-specific keywords from the job description to pass through applicant tracking systems (ATS) and increase your chances of being noticed by hiring managers.
• Proofread your resume: Thoroughly proofread your resume to eliminate errors (I recommend Hemingway App and Grammarly ). Consider seeking feedback from peers or mentors to ensure clarity and effectiveness!

Let's dive deeper into each of these so you have the exact blueprint you need to see success.

The Best Data Scientist Skills To Include On Your Resume

Keywords are one of the most important factors in your resume. They show employers that your skills align with the role and they also help format your resume for Applicant Tracking Systems (ATS).

If you're not familiar with ATS systems, they are pieces of software used by employers to manage job applications. They scan resumes for keywords and qualifications and make it easier for employers to filter and search for candidates whose qualifications match the role.

If you want to win more interviews and job offers, you need to have a keyword-optimized resume. There are two ways to find the right keywords:

1. Leverage The 20 Best Data Scientist Keywords

The first is to leverage our list of the best keywords and skills for a Data Scientist resume.

These keywords were selected from an analysis of real Data Scientist job descriptions sourced from actual job boards. Here they are:

• Data Science
• Communication
• Machine Learning
• Engineering
• Cross-Functional
• Organization
• Collaboration
• Descision Making

2. Use ResyMatch.io To Find The Best Keywords That Are Specific To Your Resume And Target Role

The second method is the one I recommend because it's personalized to your specific resume and target job.

This process lets you find the exact keywords that your resume is missing when compared to the individual role you're applying for.

Here's how it works:

• Open a copy of your updated Data Scientist resume
• Open a copy of your target Data Scientist job description
• In the widget below, paste your resume on the left, paste the job description on the right, and hit scan!

ResyMatch is going to scan your resume and compare it to the target job description. It's going to show you the exact keywords and skills you're missing as well as share other feedback you can use to improve your resume.

If you're ready to get started, use the widget below to run your first scan and get your free resume score:

Copy/paste or upload your resume here:

Click here to paste text

Upload a PDF, Word Doc, or TXT File

Paste the job post's details here:

Scan to compare and score your resume vs the job's description.

Scanning...

And if you're a visual learner, here's a video walking through the entire process so you can follow along:

Employers spend an average of six seconds reading your resume.

If you want to win more interviews and offers, you need to make that time count. That starts with hitting the reader with the exact information they're looking for right at the top of your resume.

Unfortunately, traditional resume advice like Summaries and Objectives don't accomplish that goal. If you want to win in today's market, you need a modern approach. I like to use something I can a “Highlight Reel,” here's how it works.

Highlight Reels: A Proven Way To Start Your Resume And Win More Jobs

The Highlight Reel is exactly what it sounds like.

It's a section at the top of your resume that allows you to pick and choose the best and most relevant experience to feature right at the top of your resume.

It's essentially a highlight reel of your career as it relates to this specific role! I like to think about it as the SportsCenter Top 10 of your resume.

The Highlight Reel resume summary consists of 4 parts:

• A relevant section title that ties your experience to the role
• An introductory bullet that summarizes your experience and high-level value
• A few supporting “Case Study” bullets that illustrate specific results, projects, and relevant experience
• A closing “Extracurricular” bullet to round out your candidacy

For example, if we were writing a Highlight Reel for a Data Scientist role, it might look like this:

The first bullet includes the candidate's years of experience in the role and wraps up with a value-driven pitch about how they've helped companies in the past.

The next two bullets are “Case Studies” of specific results they drove at their company. The last bullet wraps up with extracurricular information.

This candidate has provided all of the info any employer would want to see right at the very top of their resume! The best part is that they can customize this section for each and every role they apply for to maximize the relevance of their experience.

Here's one more example of a Data Scientist Highlight Reel:

The content of this example showcases a candidate transitioning from sales to data science, leveraging their experience with sales and bringing in measurable results in each bullet point. Then, they wrap up with a high-value extracurricular activity that's related to their target position.

If you want more details on writing a killer Highlight Reel, check out my full guide on Highlight Reels here.

Bullets make up the majority of the content in your resume. If you want to win, you need to know how to write bullets that are compelling and value-driven.

Unfortunately, way too many job seekers aren't good at this. They use fluffy, buzzword-fill language and they only talk about the actions that they took rather than the results and outcomes those actions created.

If you apply this framework to each of the bullets on your resume, you're going to make them more compelling and your value is going to be crystal clear to the reader. For example, take a look at these resume bullets:

❌ Data Scientist with 5+ years of experience.

✅ Leveraging 5+ years of experience in data science, specializing in predictive modeling to improve decision-making accuracy by 40%.

The second bullet makes the candidate's value  so much more clear, and it's a lot more fun to read! That's what we're going for here.

That said, it's one thing to look at the graphic above and try to apply the abstract concept of “35% hard skills” to your bullet. We wanted to make things easy, so we created a tool called ResyBullet.io that will actually give your resume bullet a score and show you how to improve it.

Using ResyBullet To Write Crazy Effective, Job-Winning Resume Bullets

ResyBullet takes our proprietary “resume bullet formula” and layers it into a tool that's super simple to use. Here's how it works:

• Head over to ResyBullet.io
• Copy a bullet from your resume and paste it into the tool, then hit “Analyze”
• ResyBullet will score your resume bullet and show you exactly what you need to improve
• You edit your bullet with the recommended changes and scan it again
• Rinse and repeat until you get a score of 60+
• Move on to the next bullet in your resume

Let's take a look at how this works for the two resume bullet examples I shared above:

First, we had, “Data Scientist with 5+ years of experience.” 

ResyBullet gave that a score of 35/100.  Not only is it too short, but it's missing relevant skills, compelling language, and measurable outcomes:

Now, let's take a look at our second bullet,  “Leveraging 5+ years of experience in data science, specializing in predictive modeling to improve decision-making accuracy by 40%”.

ResyBullet gave that a 61 / 100. Much better! This bullet had more content focused on the experience in the Data Scientist role, while also highlighting measurable results:

Now all you have to do is run each of your bullets through ResyBullet, make the suggested updates, and your resume is going to be jam-packed with eye-popping, value-driven content!

If you're ready, grab a bullet from your resume, paste it into the widget below, and hit scan to get your first resume bullet score and analysis:

Free Resume Bullet Analyzer

Learn to write crazy effective resume bullets that grab attention, illustrate value, and actually get results., copy and paste your resume bullet to begin analysis:, 3 data scientist resume examples for 2024.

Now let's take a look at all of these best practices in action. Here are three resume examples for different situations from people with different backgrounds:

Data Scientist Resume Example #1: A Traditional Background

Data Scientist Resume Example #2: A Non-Traditional Background

For our second Data Scientist Resume Example, we have a candidate who has a non-traditional background. In this case, they come from a background in sales but leverage experiences that have helped them transition to a Data Scientist role. Here's an example of what their resume might look like:

Data Scientist Resume Example #3: Data Scientist New Grad

For our third Data Scientist Resume Example, we have a new graduate who's never worked for a company before but has worked on several self-initiated projects. Here's an example of what their resume might look like when applying for Data Scientist roles:

At this point, you know all of the basics you'll need to write a Data Scientist resume that wins you more interviews and offers. The only thing left is to take all of that information and apply it to a template that's going to help you get results.

We made that easy with our ResyBuild tool . It has 8 proven templates that were created with the help of recruiters and hiring managers at the world's best companies. These templates also bake in thousands of data points we have from the job seekers in our audience who have used them to land job offers.

Just click any of the templates below to start building your resume using proven, recruiter-approved templates:

Free Job-Winning Resume Templates, Build Yours In No Time .

Choose a resume template below to get started:.

Key Takeaways To Wrap Up Your Job-Winning Data Scientist Resume

You made it! We packed a lot of information into this post so I wanted to distill the key points for you and lay out next steps so you know exactly where to from here.

Here are the 5 steps for writing a job-winning Data Scientist resume:

• Start with a proven resume template from ResyBuild.io
• Use ResyMatch.io to find the right keywords and optimize your resume for each role you apply to
• Open your resume with a Highlight Reel to immediately grab your target employer's attention
• Use ResyBullet.io to craft compelling, value-driven bullets that pop off the page
• Compare the draft of your resume to the examples on this page to make sure you're on the right path
• Use a tool like HemingwayApp or Grammarly to proofread your resume before you submit it

If you follow those steps, you're going to be well on your way to landing more Data Scientist interviews and job offers.

Now that your resume is taken care of, check out my guide on how to get a job anywhere without applying online!

Paula Martins

Paula is Cultivated Culture's amazing Editor and Content Manager. Her background is in journalism and she's transitioned from roles in education, to tech, to finance, and more. She blends her journalism background with her job search experience to share advice aimed at helping people like you land jobs they love without applying online.

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Data Scientist Resume Example, Tips & Tricks

Like every other Data Scientist around the world, you know that the job search involves more than just the collection, analysis, and interpretation of data. No matter how good you are at your job, you also need to be able to make the right first impression with prospective employers. Of course, your resume is an essential tool for putting your best foot forward - and that’s why we’ve gathered the best expert advice to ensure that you have everything you need to land an interview and convince employers that you’re the right person for their open position.

Transform your resume

This guide can help you to craft the resume narrative that you need to properly showcase your critical data research skills, analytical thinking, and talent for interpreting information. If you’re looking for help in your career advancement efforts, then our Data Scientist resume example, tips and tricks guide is the perfect tool to help you reach your goals.

How to write a resume

To successfully earn a hiring manager’s attention and interest, you need a resume that can quickly and effectively demonstrate your potential value as an employee. The following tips can help to ensure that your resume delivers the compelling narrative you need to secure an interview and eventual job offer:

Review the job description to ensure that you identify the most relevant skills and other needed qualifications

Customize your resume to focus on the skills, job experiences, and quantifiable achievements that demonstrate your qualifications for the position

Always include a professional summary statement to begin your resume - this summary should be clear, concise, and focused on demonstrating the value that you can provide to the company

Select your relevant work experience and list those companies and positions in reverse chronological order

For each job, include several bullet-point summaries of what you achieved, and use real numbers that show how those accomplishments benefited your employers

Include a skills section that includes bullet points on each of your relevant abilities - refer back to the job posting to ensure that you’re using the same words to describe needed skills. Those words are keywords that you may need if you want your resume to successfully get past any applicant tracking system (ATS) that the company might be using

You should also include an education section , depending on the requirements listed in the job posting

Review your finished document several times, proofreading for spelling, grammatical errors, and other unprofessional mistakes - remember, you only get one chance to make a great first impression!

Data Scientist resume examples

For a Data Scientist like you, a resume is your best opportunity to introduce yourself to a prospective employer in the right way. You need to ensure that your resume highlights your full range of capabilities and experiences in data collection, information analysis, and problem-solving. A powerful resume can showcase those abilities and experiences in a way that highlights your unique value as a potential new hire. Our Data Scientist resume example can provide useful tips to help your resume to present you as the best candidate for your desired job.

Professional summary

Think of the summary statement as a resume version of an elevator sales pitch. In just a few short sentences, you need to convey your abilities, experiences, and accomplishments to help the employer to understand the type of value you might provide as an employee. For example:

Future-oriented and ambitious professional with broad and deep knowledge of data science, machine learning, NLP, HPC, business intelligence, big data, and a host of cutting-edge technology areas and concepts. Goal-focused and tenacious, continually striving to deliver ground-breaking solutions to business problems through proprietary and marketable products, novel customizations, and new approaches to using and interpreting data. Capable of excelling in collaborative, leadership, and independent roles. Expertise in full-cycle project management and product development.

With this summary statement, the candidate highlights their broad range of data skills, including specific areas of focus. The summary also focuses on the employers’ needs by emphasizing the candidate’s dedication to delivering solutions and solving business problems. As a result, a hiring manager who reads it would quickly see the type of value this job seeker might bring to the position if hired.

Professional experience

Your professional experience section provides an opportunity for you to showcase your previous jobs, in reverse chronological order. For each position, include several key accomplishments in bullet point format. For example:

Performed extensive hands-on design and coding, including creating analytic solution (GUI to ML model) using the client’s proprietary framework

Designed and implemented solutions for retail, real estate, insurance, and other sectors

Successfully managed a project for a major account that helped to increase division profitability by 21% in six months

In this example, the candidate showcases their vast knowledge of technical tools while also highlighting their leadership, collaboration, project management, and other key skills. The candidate also included quantifiable results that demonstrate real value.

Key hard and soft skills for a Data Scientist

Every Data Scientist needs to include both hard and soft skills on their resume to demonstrate their capabilities. Here are some examples of key skills you may want to include:

Statistical and data mining techniques

Make sure that you include specific techniques that you’re proficient in using as you collect and analyze data. These can include hard skills like text mining, social network analysis, Random Forest, and similar techniques.

Machine learning

With the rise of artificial intelligence, machine learning skills are another set of hard skills important to anyone who works with data. Highlight your machine learning skills by referencing your proficiency with algorithms, statistics, simulations, modeling, neural networks, and similar areas of expertise.

Collaboration

Data Scientists often need to use soft skills like collaboration and communication to effectively do their jobs. This type of work may require you to effectively work with others and communicate your results to colleagues, superiors, and even clients.

Critical and/or analytical thinking

Your use of critical and analytical thinking to identify and resolve problems is an essential part of the job. These soft skills should always be highlighted throughout your resume, so that employers can better understand your value as a problem solver .

Summary and last words

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Resume writing service for Data Scientists: Let us write your resume!

Let our expert writers provide the consultation, job seeking advice, and writing assistance you need to create the most compelling resume possible. Our TopResume team understands the data science industry and knows what employers want to see from every job candidate they encounter. More importantly, they can help you to create the tailored resume you need to make that great first impression and capture an employer’s interest.

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Data Scientist Resume Example

This guide provides you with Data Scientist resume examples to use to create your own resume with our easy-to-use resume builder. Below you'll find our how-to section that will guide you through each section of a Data Scientist resume and you'll be closer than ever to landing your dream job.

Want to write a great Data Scientist resume?

You should know this. Most data science resumes that hiring managers receive scream:

• “Wrote a digit recognition algorithm with 95% accuracy”
• “Used Tensorflow to do this really simple detection”
• “Used this off the shelf software for ‘X’”

Reality is, most entry level data science resumes rarely go beyond the common pattern listed above. The experienced data science resumes on the other hand fail to communicate the complexity, scale or innovation performed.

Fixing just that would make your data science resume stand out from 90% of the other applications that a hiring manager would receive.

In this guide, we are going to take you a step ahead though. Whether you are looking to land a FAANG/MAANG data science role or work for an innovative startup - we are going to show you how to create a Data Scientist resume that will win 99% of the time!

FAANG Data Scientist Resume Example

Senior Data Scientist Resume Example

Let’s start with an overview of what it takes to create a great Data Scientist resume.

How to write a Data Scientist Resume?

To write a Data Scientist resume:

• Highlight either your business impact or data science innovation.
• Provide context to what type of ML work you performed
• Make sure to add the programming languages you use
• If applicable, show your ability to architect ML systems
• Highlight your publications

If you avoided those, you would struggle to justify how your work made an impact. For example, it isn’t uncommon for us to come across statements like these in data science resumes: “Leverage my skills in data cleaning, data analysis and predictive modeling to achieve business goals” - statements like these are bad for your resume.

However, if you are seeking an entry level data science position - consider the following while writing your entry level data science resume:

• Highlight your thesis and projects - they make a big difference when there’s no work experience.
• While listing your projects, display your thoughtfulness in approaching the problem and solving it.
• Adding programming languages adds weight to your data science resume. However, do not list yourself as an “expert” if you are a recent graduate.
• Add a link to your portfolio or Github.

Do you know about FAANG data science roles - a Github profile is the most commonly sought after resource to see how proactive you are, what you’ve built on your own and your code quality.

The Best Data Science Resume Format

The quality of a good data science resume format would be:

• A format that allows you to list your skills and experience in one (or max two pages).
• Consistent throughout leveraging not more than two fonts and shouldn’t have too many colors on it.
• Uses bullet lists instead of large paragraphs to highlight a Data Scientist’s skills and experience.

Keeping those three qualities of a good Data Scientist resume’s format, the best format for you would be:

• Reverse chronological resume format - if you are an experienced Data Scientist.
• Hybrid resume format - if you are an entry level Data Scientist who lacks the experience, but has skills and data science projects to show.

Experienced Data Scientist’s Resume vs Entry Level Data Scientist’s Resume?

What separates an experienced Data Scientist’s resume from an entry level resume is: #1 Business impact: An entry level Data Science resume can often only display a thoughtful approach to solving a problem, but a job winning Data Scientist resume should be able to show the impact of work performed.

E.g. an entry level Data Scientist resume would have “Leverage data cleaning, database management and deep learning for text classification”

Vs an experienced Data Scientist’s resume would say “Created real time text classification capabilities through hybrid deep learning models (attention mechanism position and focal loss) for City of Chicago to handle traffic violation in low light conditions. Convolution attention mechanism used was Bi-LSTM with CABO model.”

#2 Technically descriptive: As most entry level Data Scientist resumes don’t involve innovating and leveraging sophisticated technologies. It isn’t too difficult to find phrases like “Wrote a machine learning model to recognize Chinese characters”

Vs an experienced Data Scientist’s resume should say “Led digitization of 3TB of Chinese character data by using RAN of aggregation module, mapping encoder and a character analysis decoder. Outperformed existing DenseRAN by 33.6%, with 57.9% higher computing efficiency.”

As you can see, a good data science resume would change radically with the experience of a Data Scientist. But, it isn’t uncommon to see experienced Data Scientists write their resumes as if they are an entry level professional.

When you write meaningfully, a hiring manager not only is able to see the impact you made, but is also able to see if you have worked on similar business or technology projects in the past as theirs.

Data Scientist Resume: Summary or Objective?

Here’s a rule of thumb for you - write a data science resume objective only when you are an entry level professional or when you are transitioning from another role (e.g SWE) to data science. If you are already working as a Data Scientist, write a resume summary instead.

With that in mind, let’s take a look at how to write an excellent Data Scientist resume summary.

How to Write a Data Scientist Resume Summary (with Examples)

To write a great Data Scientist resume summary, include the following information:

• State your years of data science experience (e.g. 10+ years of experience in…”).
• List your top technical specialization (e.g. LSTM, GAN, etc).
• List your top business skills (e.g. customer segmentation, image processing, pricing analysis, market basket analysis, etc).
• Finally, add relevant certifications and awards that you have received.

Let’s check two examples of good and bad Data Scientist resume summary samples that will illustrate better.

Entry Level Data Science Resume Summary - Bad

I am a Data Scientist with experience of analytics and applied data science experience with a focus on strategic initiatives targeting business scalability, process improvement, and efficiency.

Entry Level Data Science Resume Summary - Professional

Data Scientist with 9 months of analytics and applied data science experience to support $100M maintenance operations using survival models and PowerBI dashboards. Business expertise: performance drift, revenue leakage and regression analysis for cost estimation.

In the two Data Scientist resume examples above, we see that both have noticeable entry level experience. But when you read the second Data Scientist’s resume summary, one can clearly see why the second data science resume would win.

If you are an entry level Data Scientist too, here’s a template that you can copy to write your resume summary: “Data Scientist with {x} {months/years} of analytics and applied data science experience to support {operations} using {data science technique}. Business expertise: {expertise 1}, {expertise 2} and {expertise 3}.”

Experienced Data Science Resume Summary - Bad

Experienced Data Scientist experienced in designing, building and deploying fast, accurate, scalable and secure machine learning applications in the cloud.

We list this as a bad data science resume summary mainly because it won’t help you stand out. Let alone beat 99% of the other data science resumes. Every word added to your Data Scientist resume allows you to leave an impact - in this case you won’t make any.

Experienced Data Science Resume Summary - Professional

Data Scientist with 10+ years of experience in building high performing NLP products. Expert at neural architecture optimization of large feature spaces for performance gains. Author of Lin-ML - used by more than 100,000+ machine learning developers.

How to Write a Data Scientist Resume Objective (with Examples)

The most important factors to consider when writing your Data Scientist resume objective are:

• Add your top skills, area of expertise or specialization in it.
• Mention what you are passionate about.
• List your top recognizable achievements.

Entry Level Data Science Resume Objective - Bad

An enthusiastic entry-level data scientist, a NCSU graduate. I have hands-on work experience in machine learning models and a portfolio of Data Science projects.

Entry Level Data Science Resume Objective - Professional

An enthusiastic entry-level data scientist with hands-on work experience in creating RNN and Modular NNs to text and speech problems. Kaggle Master, Top 5% on Stackoverflow for Python and winner of Google Universal Image Embedding challenge(GAN).

When you compare those two Data Science resume examples above it isn’t too hard to see the following:

• Good Data Scientist resumes will be very specific about their past projects and top technologies.
• Poor Data Scientist resumes will be generic or verbose without any specific skills.

Common mistakes to avoid while writing a resume summary or objective include:

• Writing more than 3 lines in a resume summary or objective. If it is a wall of text, it’s going to negatively impact your application.
• Listing yourself as an expert - it is better to let your skills and accomplishments do the job instead.
• Being too vague about your interest and technology used in projects/work experience.

The idea here is to leave a good first impression, a hook that will allow the hiring manager to continue to read further with interest.

Need more examples? Here are 6 Data Scientist resume objective examples .

How to Describe your Data Scientist Experience on Resume?

Describing your data science experience on your resume should not be taken lightly. It is always one of the top few items on a hiring manager’s checklist. Despite that importance, it isn’t uncommon to see very poorly written work history on a Data Scientist’s resume.

To write a winning Data Scientist resume, you should describe your experience by following the STAR method. Using the STAR method it is very easy to highlight a problem you solved, how thoughtful you were in solving the data science problem and what results you achieved.

Let’s checkout a couple of examples to see how

Bad Data Scientist Resume Experience Sample

Data Scienstist

• Worked within the Data Science team in the SF office.
• Taking responsibility for coordinating data partnerships, and improving existing modeling processes.
• Spearheading data for new lines of business.
• Support internal data modeling needs for stakeholders and cross functional teams.
• Utilizing a plethora of technologies in my day-to-day work.

Looking at this Data Scientist’s resume, any hiring manager would wonder:

• If they have the right experience to solve the data science challenges they are looking to solve?
• They failed to communicate the impact of their work - would they be able to communicate their insights in a way that everyone can understand?
• What functions did they serve in this role?

Hiring managers spend as little as 7 seconds scanning a resume. They scan your summary/objective, job titles, work experience and your skills. If they don’t find what they are looking for, they discard your application - all in 7 seconds!

That’s why we suggest you write your work history section in a way that reduces their efforts to find the information they are looking for and leave an impact at the same time.

Let’s now look at a few examples of work history sections of good data science resumes.

Data Scientist Resume Work Experience

Data Scientist

• Optimized existing geospatial query to improve performance by 20%.
• Cleaned car image data with 10,000+ different types of cars to create a new vehicle identification API supporting over 80,000+ car dealerships.
• Worked with compliance teams to implement an AI algorithm (entity resolution algorithm) to protect against cyber threats.
• Data Science lead for DPro (dealer product) initiatives and managed ~20+ data science initiatives.
• Tech stack used: Pandas, PySpark, MCMC, GCP, Databricks, and SQL

Machine Learning Data Science Resume Work Experience

ML Data Scientist

• Created multiple deep neural network architectures to improve robotic instrument segmentation.
• Saved $15.3M in annual spend by deep learning focused histology image analysis with 93.8% accuracy.
• Implemented U-net architecture replacing existing ImageNet neural network with 10.9% higher performance. Consumed by $200M LOB products as of 2022.
• Restructured internal database of >3TB production records to improve performance.

FAANG/MAANG Data Science Resume Work Experience

Meta Data Scientist

• Identified top metrics, collected data, modeled data using SEM, and provided recommendations for the operational performance of 20+ Meta data centers located throughout the world.
• Drive Advertiser value through LSTM implementation and improve the existing understanding of Facebook’s system understanding.
• Risk control - 8.5% higher click-farm identification which led to $10M in wasted ad spend from advertisers.
• Key partner for the product team to collaborate on new insights for the Advertiser product portfolio.

How to Write a Data Science Resume With No Experience?

When you have no data science specific experience, consider writing a section that focuses on your portfolio of data science projects instead. The type of projects that you can include are:

• Recognizable competitions like Kaggle
• Projects listed on your Github profile
• Any significant academic projects performed

Platforms like Kaggle are often used by companies that are hiring entry level/experienced data science talent. And, your Github projects will enable an employer to see what you are capable of, along with your code quality.

Companies like Uber, Microsoft, etc actively collaborate with universities in the form of academic partnerships. That’s why academic data science projects bring in substantial weight to your data science resume for a hiring manager.

How to List your Data Science Projects on Resume?

To list your data science projects on your resume, create a separate section for your projects. For each project add the following information:

• Title of the project
• Short description of the project involving the problem you solved, the solution you used and technology involved.

Data Scientist Resume Example - Projects

Instacart Market Basket Analysis Model building - used XGBoost with two gradient boosted tree models (predicting reorders, predicting zero orders). Characteristic of each of these models include:

• Reorder model - XGBoost with 6 gradient boosted tree models (GBDT, random seed)
• Zero order model - XGBoost with 17 boosted tree models (with a step shrinkage)

Project insights involved:

• Identified patterns where a user won’t repurchase an item.
• Days since reorder plays an important role.
• Items reordered more frequently vs those that aren’t.
• When a user is unlikely to make a reorder.

How to List Your Education on your Data Science Resume

To list your education on your Data Scientist Resume create a new section for education and list your education credentials in it. Your education section should be concise if you are not an entry level Data Scientist.

Example Education Section in an Experienced Data Scientist Resume

Masters in Data Science, 3.9 GPA Texas A&M University

BS, Data Science, 4.0 GPA Texas A&M University

Example Education Section in an Entry Level Data Scientist Resume

• Coursework taken: Big Data 101, GeoSpatial Computing 309 and Machine Learning.
• Thesis: Leveraging GeoSpatial computing with LIDAR data to predict flooding for urban environments.
• Elected as President of Texas A&M Data Science club of 500+ members.

Top 20 Data Science Resume Skills for 2022

• Machine Learning
• Deep Learning
• Data Visualization
• Neural Networks
• Distributed Computing

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The Complete Data Science Resume Guide in 2024

Join over 2 million students who advanced their careers with 365 Data Science. Learn from instructors who have worked at Meta, Spotify, Google, IKEA, Netflix, and Coca-Cola and master Python, SQL, Excel, machine learning, data analysis, AI fundamentals, and more.

Recruiters go through hundreds of applications daily, so writing a data science resume that makes an impression is challenging.

Large enterprises like Google receive more than two million applications a year . Nearly all prominent corporations—including over 98% of the Fortune 500 —use Applicant Tracking Systems (ATS). That’s one more barrier your resume needs to jump.

So, how do you write a captivating resume that will land you a data science job interview?

You’ll learn everything you need in this article. Feel free to jump to the sections that are relevant to you.

The Complete Data Science Resume Guide in 2024: Table of Contents

• Data Science Resume Best Practices 
• Types of Resumes for Different Experience Levels
• Entry-Level Data Science Resume
• Data Science Resume for Career Switchers
• Senior Data Scientist Resume
• Do’s and Don’ts in a Data Science Resume
• Resume vs CV
• Should You Use Professional Resume-Writing Services?
• The Best Resume Builder Websites and Resources
• How to Build Your Digital Presence
• The Data Science Resume Writing Process

1. Data Science Resume Best Practices

Regardless of your experience, background, and goals, there are universal rules to follow when creating a resume.

Tailor Your Resume to the Job Offer

For starters, forget the generic resume——tailor it to the position and company you’re applying for. Your resume must meet the employer’s expectations and demonstrate that you’re a data science professional with the right mindset , qualifications , and skills for the job. A successful data science resume contains keywords matching the skills and competencies listed in the job description.

Use Numbers and Metrics

Recruiters seek experience, a specific degree, and skills that match the description. Simply listing your competencies isn’t enough. You must back them up with numbers and details that highlight and add credibility to your accomplishments .

Use Strong Action Verbs

Strong action verbs demonstrate you’re a doer and achiever. For example, instead of being "responsible for data analysis," write that you "executed the (X/Y) data analysis project." To make your achievement even more convincing, add the project outcome: "Executed a customer churn analysis project that led to a 15% increase in retention rates." Format Your Resume Professionally

Once you’ve covered the essentials, you should fine-tune your data science resume to add a professional touch.

Don’t underestimate the power of a consistent, pleasing-to-the-eye format and a clean look. Your data science resume must be appealing, concise, easy to read, and mistake-free.

And make it brief. Employers love a concise resume highlighting the qualifications, skills, and experience needed .

2. Types of Resumes for Different Experience Levels

The sections, outline, writing style, and format of your resume may vary depending on where you are on your data science career path .

The following section is dedicated to those new to data science, especially recent college graduates and professionals transitioning from another field.

We’ll then continue with valuable insights for those with rich experience who wish to move up the data science career ladder or switch employers.

Types of Resumes

• Skills-based/functional resumes focus more on your capabilities and achievements and less on your work experience. It’s preferred for junior professionals, career switchers, and college students at the beginning of their data science career journey.
• Chronological resumes are work experience-oriented. This is the best option for professionals who have held multiple related positions. If you're a recent graduate who doesn't have years on the job, a chronological data scientist resume will only bring attention to this weakness.
• Combination/hybrid resumes are ideal for career switchers and candidates with relevant work experience. It combines elements of the other two formats, allowing you to emphasize your capabilities and experience equally. Even if you haven’t worked as a data scientist, this enables you to showcase your transferrable skills.

There’s no right type of resume. Choose the one that highlights your competencies in the best way.

3. Entry-Level Data Science Resume

This section is devoted to those who have just graduated from college or university and wish to start building a data science career. Discover what you need to craft a resume that will get you a job interview for your first entry-level data science position.

Once you achieve this, we’ll help you prepare for your data science interview .

How to Write a Skills-Based Resume

Write down your relevant experience, including education, internships, job-specific skills, and data science projects. Once you list everything that comes to mind, start organizing the content. Don’t initially become preoccupied with consistent formatting or details when creating an entry-level data science resume.

Resume Sections

A functional data scientist resume typically has an extensive skills section, including a list of your capabilities with a brief description of how you’ve obtained and applied them. The rest depends on your experience—you can list previous jobs, volunteer work, relevant projects, education, languages, interests, etc. Adding a summary is also a good idea.

When organizing your resume, put your skills and achievements first, then continue with data science projects you’ve worked on, and finally, include your relevant employment history and education.

The sections’ order, however, is not set in stone. Tailor the data science resume to your experience and the job requirements. For example, the astrophysics club may not be relevant to the data analyst position in a financial corporation, but it could be a great asset for a software engineer internship.

Resume Headline

Your name should be the first item on the page, followed by a headline briefly describing your experience, education, current job title, and areas of interest. Although the headline represents you, ensure it applies to the job you’re applying for. Keep it short and relevant—a few words or a short sentence fragment are more than enough. Contact Information

The next element on your data science resume should be your contact details, which include your full name, phone number, and professional email address. You can also add links to your LinkedIn, GitHub, Kaggle profile, or other platforms that demonstrate your data science adeptness.

Data Science Resume Objective vs Profile Summary

A data scientist resume can contain an objective or a profile summary section. While the two are similar and sometimes used interchangeably, some key differences exist.

The objective statement is slightly shorter and closely related to the position you’re applying for. It highlights your professional goals and how you would contribute to your desired position and company.

In contrast, the summary focuses on your qualifications. It emphasizes the skills, experience, and achievements that make you a good fit for the job.

These are optional sections, so if you include one, ensure it’s worth the space . Tailor them to the company and position, mirror the language of the job posting, and highlight your most significant strengths.

Although some claim that the objective statement is outdated, if you’re applying for an entry-level job or internship , your data science resume may benefit from such a section.

Objective Statement Examples

• Result-oriented individual with a strong capacity for learning and a bachelor’s in computer science. Seeking to utilize hands-on modeling experience as an entry-level data scientist at BCG Gamma. Possessing expert knowledge of scripting languages and the ability to work in a cross-functional environment.
• A highly analytical economics graduate with strong interpersonal and leadership skills. Possessing a solid statistics background, programming skills, and ability to communicate complex and industry-specific concepts. Looking to apply superior analytics skills as a data analyst at IBM.
• Seeking to gain model development experience and a strong understanding of research design and hypothesis testing as a data scientist at Appsilon Data Science. Providing programming skills and the ability to analyze complex data. A team player with strong communication skills and data science certification.

The education section may appear earlier in your data science resume if you’re an aspiring data science professional who has just finished college. If you’ve graduated with one of the most in-demand degrees for data scientists , you can add it below the contact information or the objective section.

State the name of the college or university, city, state, and degree (data science, statistics, computer science, engineering, etc.). Include your major(s) and minor(s) and the year and month you completed your degree (or expected date of completion). List the highest or most relevant first if you have more than one degree. US graduates can also include their GPA score (optional).

Data Science Projects and Publications

How do you include project details in your resume? This section allows you to compensate for the absence of rich professional experience. You can include significant data science-related coursework and academic projects you’ve completed.

But more importantly, showcase any side projects you’ve worked on to demonstrate initiative and ability to work independently. Add links to GitHub projects in your data scientist resume so potential employers can see what you’ve created and how you’ve done it.

It’s natural to feel anxious if you lack relevant work experience. But there are meaningful ways to fill in this section and plenty of entry-level positions that don’t require years on the job. You can include clubs and societies you’ve actively participated in, internships, academic research, volunteer work, etc.

Choose relevant headings for the listed experiences and add the most relevant ones first. Format them as you would with any work experience—including the name and location of the organization, your role or title, and the period of your work. Add two to five bullet points per experience demonstrating how you applied your skills to the assignment or a project.

This is the most essential part of the functional data science resume. How do you stand out without years of experience? Employers are seeking transferable skills in fresh-out-of-college applicants for entry-level data science positions.

Transferable skills indicate you have what it takes to succeed in a given role. Many of the skills required for data science positions are universal:

• A sharp eye for detail
• Identifying issues and developing effective solutions
• Understanding and confidently presenting technical information to non-technical audiences
• Initiative and ability to learn quickly and work efficiently
• Planning, organizing, and managing multiple projects with competing demands and deadlines
• Being a team player and interacting with employees of all levels of the organization

List all relevant skills to the position and illustrate how you’ve applied them.

Honors and Awards

Honors and awards can also be a stand-alone section on your data science resume. List the name of each honor or award and the date you received it. A brief description emphasizing your accomplishment is optional.

Certificates

Adding data science certificates provides additional credibility to your resume, proving you’re qualified for the position—even if you don’t have much relevant experience. They also demonstrate ambition and commitment to developing industry-relevant and in-demand data science skills.

Updating your data science resume with newly acquired certificates is good practice. If you don’t have a certificate, consider signing up for a data science training program, upgrading your skillset with the specific position in mind.

Volunteer Activities and Community Involvement (Optional)

You can showcase your participation in various on- and off-campus communities. Include the positions you’ve been appointed to, the organizations’ names, locations, and dates. In addition, you can highlight a ‘study abroad’ program you’ve been enrolled in and prominent volunteer experience.

Interests (Optional)

Interests is an optional section. Many employers would like to know more about you outside of your education and job-related experience. This helps them determine what kind of person you are and whether you’d fit the company culture.

But this section should be your last priority. Include it only if your interests are relevant to the position or organization. Be genuine but keep it professional.

Data Science Resume Writing Style

You should follow a few style guidelines to write an impressive resume. First, keep in mind that this is your first introduction to a prospective employer. So, take your time to make it visually appealing and error-free.

Second, be brief and concise. Include only relevant information to the data science position that underscores your qualifications. Don’t overload it. Your resume may get only 15 to 30 seconds of consideration.

Your resume should be:

• Straightforward and comprehensive
• Clear and concise
• Professional and grammatically correct
• Factual and accurately describing your accomplishments
• Written with action verbs

A good data scientist resume utilizes a good dose of power verbs. Refer to this list of action verbs for inspiration.

Now that you know how to write an engaging resume, let’s discuss formatting.

How to Format Your Data Science Resume

After spending long hours writing your resume, the last thing you want is for it to end up in the rejection pile. So, what should it look like to make a good first impression?

Adequate spacing, proper alignment, and neatly organized content are mandatory. And make sure everything fits onto one page.

But how do you create an appealing format?

There are plenty of options online. Most candidates use standard one-page templates. You can download our simple yet stylish data science resume sample and fill in your information.

You could also select a more elaborate format for your data scientist resume. But consider the type of job you’re applying for. Don’t go overboard with a flashy resume design and intricate elements.

And if you wish to design your resume from scratch, follow these formatting tips.

Resume Header

Section headings should be left-aligned and prominent but not distracting. You can bold or capitalize, use italics, or underline them—breaking up the document length and creating emphasis. And remember to be consistent. Stick to the same formatting for identical pieces of content—e.g., bold for the organizations you’ve worked for, italics for the job titles, and so on.

Your data science resume must be easy to read.

So, choose a font size between 10.5 and 12. The only part that should be larger is your name at the top. If your text is size 12, use 14 or 16 for your name.

Choose a font that’s easy to read on- and off-screen. It’s a means to deliver your message, so it shouldn’t be distracting.

Resume Length

The standard resume length is one page, especially for recent graduates and young professionals. You may need two pages if you have a significant amount of relevant experience, advanced degrees, or publications.

But mind that recruiters typically spend only seconds per resume. Anything longer than one page may discourage them from reading it. So, it’s better to narrow it to the crucial information and save the rest for the interview.

Your data science resume should be easy to scan. So, use appropriate margin size, sufficient spacing, proper alignment, and bullet points.

Should You Include a Photo in Your Data Science Resume?

The requirements to add a photo to your resume vary depending on the country.

While the UK, Ireland, Canada, Australia, and the US don’t require a pic, it’s highly recommended in most European countries, including Austria, Belgium, France, Germany, Portugal, and Spain, as well as the Scandinavian countries, the Middle East, Africa, Asia, and South America.

4. Data Science Resume for Career Switchers

As an experienced professional, you’ve already sent many resumes throughout your career. But what if your work history has nothing to do with data science, and you wish to transition into the field?

What’s the best type of resume, skills-based, chronological, or combined?

If you switch to data science from a different industry, a chronological resume will make you look like the wrong person for the job. A combined resume is your best option.

Sure, the skills-based data scientist resume is designed for those who don’t have relevant work experience. But you don’t want to divert the spotlight from your work history completely.

With a combined resume, you can demonstrate the transferable skills you’ve gained through your previous experience.

How to Write a Combined Resume

The key to writing a winning data science resume for career switchers is to tailor it to the job requirements. Instead of focusing on your previous experience, employ the mirroring technique by taking all the keywords and phrases used in the job ad to describe the ideal candidate and integrating them into your resume.

You can further align your skills and career aspirations with the company’s goals and needs. (You can find them in the company’s mission statement.)

Now, let’s go over the essential resume sections.

Your headline must match the role. If you don’t have experience in a similar position, use your desired role as a headline—e.g., Aspiring Data Scientist. This will grab the hiring manager’s attention and help you pass the Applicant Tracking System’s (ATS) scan.

Contact Information

This is straightforward: Add your name, phone number, professional email address, and links to your LinkedIn, GitHub, and Kaggle profiles.

Data Scientist Objective Statement or Summary

The summary focuses on the individual’s experience and qualifications. And while that’s crucial, an objective statement might be more suitable for a career switcher’s resume.

The objective must convey enthusiasm and be tailored to the specific data science position. It also showcases your strengths and capabilities but focuses on how you’ll bring value to the organization.

This is the most crucial section in your data science resume. To make it work in your favor, determine the relevant skills for the position.

All job postings include keywords describing the top skills employers look for. And your resume will probably be rejected if it doesn’t contain any of them.

Many employers utilize Applicant Tracking Systems (ATS) that scan resumes for keywords and automatically eliminate those that don’t contain them. Hiring managers also look for words and phrases that match the job requirements.

All required skills have already been communicated in the job posting. You just need to use them in your resume.

Carefully evaluate your level of expertise in each area. Select three to five essential skills you feel the most confident in and list them in your data science resume. Then, provide relevant, quantifiable evidence of how you’ve obtained and applied them.

Forget the age-old clichés like trustworthy, dynamic, a problem-solver, great communicator, etc. They seem meaningless unless you also write how you’ve demonstrated those skills and how they apply to your desired job.

Remember that organizations are goal-oriented. List the qualities and skills that translate easily across various industries and contexts and use them to show the value you can provide.

Work Experience

In this section of your data science resume, you can add your previous positions like you would for any application, including the dates, job titles, and company names. But instead of listing the tasks you’ve performed, emphasize the transferable skills you’ve gained.

Quantify the experiences to convey the scale of the projects you’ve worked on and your achievements, making a stronger impression. Include the following instances:

• Established new strategies or procedures
• Used resources effectively (e.g., reduced expenses)
• Demonstrated effective project leadership or management
• Managed or supervised others efficiently
• Received promotions and expanded my scope of responsibilities
• Increased profits and improved the company’s services

And be prepared to answer many follow-up questions for these achievements during the data science interview.

Consider the advice of Edouard Harris —a physicist turned successful data scientist and co-founder at SharpestMinds :

If you’ve worked in finance, leverage your knowledge in finance. Don’t erase your past. Don’t say, “ Oh, I was in finance but not anymore .” No, no, no. You want to be like, “Yeah, I was in finance. I was goddamn good at it, and I worked on x, y, and z when I was in finance. And now, look at how I’m applying x, y, and z in interesting and clever ways as appointed to this instance .” You want to tell a story and create a narrative about yourself. And the narrative you create about yourself when you transition isn’t, “ Oh, I’m changing everything about myself .” It’s more like, “ No. I’m moving away to even further increase the value of the experience that I already have . ”

Relevant Certificates/Education

The competition for data science positions is intense. That’s why you need proof that you have the necessary skills for the job. But returning to university to earn a relevant degree could be quite challenging for a working professional, even if you’re in-between jobs.

Fortunately, alternative ways exist to learn data science at your pace. 365 Data Science’s program covers everything you need to become a certified data scientist—from the fundamentals to advanced topics. And adding a certificate of achievement will give your data scientist resume credibility.

Of course, you should add your education, which is still valuable even if it isn’t in a related field.

Next, add all the data science projects you’ve participated in, showcasing your technical skills. Describe the tools and techniques you used and the projects’ outcomes. But don’t overcrowd the section with technical terms. After all, it should be accessible to hiring managers with a non-technical background.

Make this section of your data science resume brief but rich in content. Don’t cover the project in detail; you’ll have the opportunity to discuss it during the interview. Focus on your contribution and achievements.

Honors and Awards (Optional)

Add this section only if your accomplishments are relevant to the position you’re applying for or if they highlight skills that are invaluable to the employer.

Data Scientist Resume Writing Style

Use powerful, action words to make your resume impactful—e.g., instead of “responsible for,” use “managed.” The former doesn’t reveal your involvement in the task or project, depriving you of any credit. In contrast, “managed” conveys you possess leadership skills.

Back this up with numbers, facts, concrete outcomes, and achievements to increase the desired effect. For instance, writing that you managed a project that led to a 47% increase in profits demonstrates that your leadership results in profit growth, and you get well-deserved credit for that.

Of course, keep everything moderate and be realistic and truthful about your achievements. More importantly, include only things you feel confident discussing during the interview.

5. Senior Data Scientist Resume

If you’re a data science professional who wants to climb the ladder, your resume will look different from that of an intern or junior specialist. It’ll also vary depending on whether you want to start a job at a new company or if you’re applying to your current one.

If you wish to switch positions at your current workplace, it’s crucial to highlight all your contributions to the company.

Emphasize your achievements, the issues you’ve resolved, and the projects you’ve participated in. But through all this, highlight how you’ve obtained and applied the skills necessary for the new role. Otherwise, you risk getting in the “you’re too valuable in your current positions” pile.

If you wish to land a job at a new company, highlight your achievements throughout your career.

Make your data science resume focused and concise. This can be difficult if you have vast experience but try to narrow it down to only the most relevant roles and achievements to the new position.

In both cases, you should aim to craft a resume demonstrating your suitability for the role. Emphasize how you’ve obtained and applied the required skills. Include quantitative evidence of your achievements to illustrate your value to your future employer. For instance, state by what percentage you made a process more efficient, how much revenue you helped generate, etc.

The recruiter may ask you to elaborate on your accomplishments during the data science interview . So, be prepared to back your claims by explaining the situation, actions, outcomes, and how you achieved them.

Which data science resume type is suitable to achieve this?

How to Write a Chronological Resume

The most suitable format for a professional with extensive experience is the chronological resume. It lets you focus on the gained experience and creates a narrative of your career progression.

But it may not be the best option if you have long periods of unemployment or have changed your field of work. A combined or skills-based data science resume is more appropriate in these cases.

The most vital section in the chronological resume is work experience, which you list chronologically—typically from the latest to the oldest position.

Header and Contact Information

If you’re a seasoned data science professional with much to add to your resume, you can skip the summary or objective statement. The summary, however, will add value to your resume. Of course, start with your name, header, and contact information.

Data Science Resume Objective or Summary?

The objective statement aims to demonstrate how your career aspirations meet the employer’s requirements. But a senior professional’s career goals typically become evident from their experience, which makes the objective obsolete.

A summary allows you to create a narrative about your expertise. You can highlight your most valuable qualifications and achievements and give recruiters an idea of your capabilities.

As previously stated, boost your data science resume using the keywords mentioned in the job description, making your resume ATS-friendly. Identify the sought-after skills, select the ones you feel the most confident about, and highlight them in your summary.

You can also go a step further and analyze your target industry and company. Identify your key selling points and tie them to the company issues you’re qualified to solve. Find where your goals meet your target organization's needs and use this to your advantage.

Unlike the skills-based data scientist resume, the chronological one doesn’t need an extensive skills section with bullet points and long descriptions.

Still, it’s a good idea to list the tools and techniques you feel most confident about, especially the ones mentioned in the job description. Of course, you should be able to demonstrate how you’ve mastered them—e.g., via your experience or certification programs.

This is the largest and most important section in your chronological data scientist resume. There is the challenge of trying to fit everything onto one page. But it’s better to omit some of your previous jobs than to have a lengthy resume that no one wants to read.

So, start with your most recent job, then list your previous roles chronologically. If the list is too long, you must make some tough decisions. Ask yourself for every job you list if it’s relevant to the current role. If you’re unsure, don’t include it.

It’s more important to leave space for describing your responsibilities and accomplishments than to list all titles you’ve held. If you haven’t included all your previous jobs, call this data science resume section Relevant Experience.

Instead of listing everything you did in a given role, add three or four bullet points per job describing the tasks and accomplishments relevant to the new position. With each responsibility you describe, add the outcome for the company and a metric to support your contribution.

Relevant honors and awards support your resume and give you the extra edge it needs to stand out. Include academic awards and accolades because it's an excellent way to demonstrate that your work and contributions to the data science field have been recognized.

Trainings/Certifications

Data science is a rapidly changing field. So, demonstrating that you continue to learn and upskill is crucial. Your data science resume will undoubtedly benefit from adding role- and industry-specific training, conferences you've attended, or seminars you’ve conducted.

The Interests section is more effective than many people think.

If something that separates you from the rest and demonstrates a transferable skill—like sports achievements—consider adding it to your resume. But do not include random hobbies that don’t show a relevant quality or capability.

When it comes to resume writing style, less is more.

Eliminate words like ‘numerous,’ ‘approximately,’ and ‘around.’ Instead, use specific numbers and remove redundant pronouns and articles, which make your resume heavy without contributing to its content. Your resume should be simple yet sharp and precise, conveying professionalism and confidence.

6. Dos and Don’ts in a Data Science Resume

No matter how experienced you are in resume writing, making careless errors is easy. Avoid common mistakes by following our recommendations for a job-winning resume.

7. Resume vs CV

What’s the difference between a resume and a curriculum vitae (CV)?

• Resumes are brief, overview your skills and experience, and are tailored to a specific job posting.
• CVs are more detailed (and lengthy) and used explicitly for academic applications.

But note that only the US and Canada make this distinction. In Europe, a CV and a resume are interchangeable, entailing a short document targeting a specific job.

So, the rules for writing a data science resume described above apply unless you’re applying for an academic job in the US or Canada.

8. Should You Use Professional Resume Writing Services?

If the thought of writing a data science resume still frightens you, you can hire a professional service to do it for you. But before choosing a company, conduct detailed research online to ensure it’s a quality service. You then submit the relevant information about yourself, and the firm delivers a resume.

• A professional-looking resume
• No worries about typos and grammatical errors
• Optimal structure for your purposes
• Your data science resume quality is not guaranteed—the outcome is as good as your provided information.
• Need to modify the resume for different job applications—may not have access to the template.
• Could be expensive

Some resume writing companies offer an additional service of resume review and feedback from a professional. So, you can write your resume applying the above principles and submit it for review, which is much cheaper, and you’ll receive advice for areas of improvement.

9. The Best Resume Builder Websites and Resources

There’s a wide choice of resume builders available online. But how do you choose the best one? Check out our list of resume builders that offer the best features and valuable, relevant resources.

• ResumeGenius
• ResumeBuild

10. How to Build Your Digital Presence Using LinkedIn, GitHub, and Kaggle

Job search sites aren’t the only way to find and apply for new roles. Many online platforms exist for professionals to build a network and even find jobs. Used correctly, they are an excellent opportunity to establish yourself as an expert.

Plus, adding links to your LinkedIn, GitHub, and Kaggle profiles to your data science resume allows employers to learn more about you and your work.

But how do you use these links optimally?

How to Optimize Your LinkedIn Profile

LinkedIn is the go-to platform for professional networking, data science job searching, and establishing your online presence as an expert.

In addition, you can learn more about the companies you’re interested in, stay updated on news in your field, and explore career opportunities. It’s also an opportunity to get noticed by recruiters.

Think of your LinkedIn profile as your online data science resume. The platform is an indicator of how thorough your profile is. Follow our tips on further improving it to appear in more searches.

Professional Headline

First, make your headline brief and memorable by highlighting your skills and achievements with strong, impactful words.

Customize your URL using your name or an abbreviated version, making it easier to remember and share. And it will look better on your data science resume.

Choose a high-quality photo in business attire and one where you’re alone, avoiding distracting backgrounds.

Your LinkedIn summary should be similar to your data scientist resume summary. Structure it as a brief first-person narrative (six to nine lines) describing who you are and what you do. Include your education, relevant experience, competencies, and career goals.

You can add your entire job history or only the relevant experiences here. A one-page format doesn’t limit you; you can describe your responsibilities and achievements in more detail. Still, it’s better to be brief and concise.

Add an education section and include your degrees. If you’re at the initial stages of your career and lack sufficient experience, you can have additional information about your completed subjects and projects.

Most online certification programs allow you to add certificates to your LinkedIn profile, which gives extra credibility to your skills. Another way to prove your skills is by completing LinkedIn’s free assessments.

Should You Add Your GitHub Page to Your Data Scientist Resume?

Absolutely. Every data science professional needs a GitHub profile. Adding it to your resume is a great way to showcase your skills and work.

Your project doesn’t have to be extraordinary to make it to your data science resume. If you’re happy with the code, publish it. It’ll show employers you have the skills and motivation to complete side projects.

And ensure your code follows the best practices. Companies prefer to hire a specialist who writes good, clean, and well-tested code.

Should You Add Kaggle Competitions to Your Data Science Resume?

Participating in Kaggle competitions doesn’t automatically turn you into an expert. Still, it broadens your experience and enhances your skills. And this is particularly important if you lack job experience.

11. The Data Science Resume Writing Process: Final Words

While it requires substantial preparation and work, resume writing can be pleasant. It lets you step back, view your experiences differently, and create your ideal narrative.

Determine the crucial skills for the job and showcase how you’ve obtained and applied them. Consider how hiring managers perceive your qualifications and accomplishments and convince them you can bring value to their business. Finally, ensure your layout, grammar, and formatting are impeccable to make a great first impression.

And remember that your data science resume will always be a work in progress that changes and develops as you upskill and gain experience.

Are you excited to begin your data science career?

Our course on Starting a Career in Data Science: Project Portfolio, Resume, and Interview Process will help you take the next step to land your dream job. Sign up for our learning program and try the course for free.

Marta Teneva

Senior Copywriter

Marta is a former Senior Copywriter at 365 Data Science. Digging into her own experience of transitioning into a new field and all the uncertainty that initially goes with it, she creates informative and fun to read content that helps our readers expand their career options in data science and achieve the goals they have set for themselves.

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6 Data Scientist Resume Examples for Your 2024 Job Search

Data Scientists have an analytical eye and love to break down complex theories and hypothesis into tangible solutions. As a Data Scientist, your resume should track data in an insightful way that delivers an impact just like your solutions do. In this guide, we'll look at 6 Data Scientist resume examples to help position yourself for success in 2023.

Resume Examples

Resume guidance.

• High Level Resume Tips
• Must-Have Information
• Why Resume Headlines & Titles are Important
• Writing an Exceptional Resume Summary
• How to Impress with Your Work Experience
• Top Skills & Keywords
• Go Above & Beyond with a Cover Letter
• Resume FAQs
• Related Resumes

Common Responsibilities Listed on Data Scientist Resumes:

• Develop data mining algorithms and techniques to discover hidden insights from vast amounts of structured and unstructured data.
• Build and deploy machine learning models for predictive analytics.
• Extract, wrangle, and clean data from various sources.
• Research new technologies and solutions to enable data science projects.
• Create interactive data visualizations and summaries to present complex information.
• Analyze and interpret data using descriptive, predictive and prescriptive analytics.
• Work in partnership with stakeholders and other teams to deliver data science solutions.
• Evaluate effectiveness of models and suggest solutions for improvement.
• Develop and implement automated methods and scripts to collect, analyze and report on data.
• Test and deploy models into production environment.
• Lead initiatives to improve identification and correct sources of data quality issues.
• Guide stakeholders on best practices for extracting, combining and validating data.

You can use the examples above as a starting point to help you brainstorm tasks, accomplishments for your work experience section.

Data Scientist Resume Example:

• Developed and implemented machine learning models to improve customer retention, resulting in a 15% increase in customer retention.
• Collaborated with cross-functional teams to develop predictive models to improve business outcomes, resulting in a 20% increase in revenue.
• Led a team of 3 data scientists to develop and implement data-driven solutions to improve business outcomes.
• Created and implemented predictive models to improve customer acquisition, resulting in a 10% increase in new customer acquisition
• Developed and implemented natural language processing models to improve customer service interactions, resulting in a 15% reduction in customer complaints
• Conducted data analysis to identify patterns and trends in customer behavior
• Assisted in the development and implementation of machine learning models.
• Conducted data cleaning and preparation tasks.
• Collaborated with data engineers to develop data pipelines to improve data quality and accessibility.
• Machine Learning
• Predictive Modeling
• Data Analysis
• Data Cleaning and Preparation
• Data Pipelining
• Data Visualization
• Natural Language Processing
• Statistical Modeling
• Algorithms and Optimization
• Big Data Platforms
• Cloud Computing
• Team Leadership
• Business Outcomes Improvement
• Database Design
• Data Mining
• Data Science
• Mathematics

Data Science Fresher Resume Example:

• Developed multiple prototypes and datasets for machine learning applications using Python, R and other languages.
• Constructed numerous data visualizations for statistical analysis and discovered meaningful data insights.
• Presented research findings to peers and management, in a clear and efficient manner, thus increasing general organizational understanding of the data.
• Authored documents and reports to explain complex data analysis results to the wider public
• Attended various conferences and seminars to enhance knowledge of data science and machine learning trends
• Database cleaning and organized production of large datasets for pattern and trend recognition
• Constructed predictive models and algorithms to discover new data collection methods
• Created and validated experiments to gain in-depth knowledge of data-driven solutions
• Spearheaded development of technical documents, which required intense data mining techniques
• R Programming
• Statistical Analysis
• Algorithm Development
• Database Management
• Database Optimization
• Technical Writing
• Presentation & Communication Skills
• Project Management
• Research Methodology
• Artificial Intelligence

Data Science Intern Resume Example:

• Utilized data science tools and techniques to quickly familiarize with the company's datasets and data structures.
• Developed actionable insights from datasets by identifying trends, correlations, and repeatable processes.
• Created predictive models and visualizations to accurately forecast future outcomes, aiding senior leaderships' decisions.
• Leveraged AI, programming languages, and database systems to drive fast and accurate results in data science projects
• Generated up-to-date reports communicating organizational findings, conveying context and relevance effectively
• Applied statistical analyses to evaluate current business performance metrics and draw conclusions
• Streamlined data workflow by cleaning, transforming, and importing data into the company's systems
• Constructed data models, in collaboration with other teams, to enhance organizational insight and potential
• Spearheaded initiatives to provide better and more reliable communication of data analytics to stakeholders
• Creative problem solving
• Proficiency in programming languages (e.g. Python, R, SQL)
• Statistical analysis
• Data mining
• Machine learning
• Data cleaning & transformation
• Data visualization
• Big data manipulation
• Project management
• Technical communication & reporting
• Data driven decision-making
• Business analytics

Entry Level Data Scientist Resume Example:

• Improved database models and querying techniques, increasing query efficiency by 20%.
• Applied machine learning models to forecast customer demand, enabling business to better manage inventory levels.
• Enhanced reporting solutions by developing an innovative data visualization platform, resulting in a 10% increase of meaningful analysis efficiency.
• Automated data analysis pipelines, reducing manual processes and errors by 10%
• Developed A/B tests and experiments to measure the effectiveness of data-driven decisions, leading to a 25% improvement in effectiveness
• Spearheaded the implementation a cybersecurity protocol, safeguarding data and maintaining secure operations
• Built customer segmentation models to enhance the organization’s knowledge of customer demographics and preferences
• Processed and prepared large data sets from four different sources, merging the data into one comprehensive database
• Constructed comprehensive data dashboards for the effective and timely visualization of data, increasing work efficiency by 20%
• Database Modeling
• A/B Testing
• Cybersecurity
• Segmentation Modeling
• Data Preparation
• Data Dashboards
• Data Wrangling
• Programming
• Logical Thinking
• Communication
• Problem Solving
• Time Management
• Attention to Detail

Junior Data Scientist Resume Example:

• Achieved a 20% increase in overall efficiency by revamping existing queries and data models built in SQL and R
• Used a combination of Python and Tableau to develop dynamic dashboard visualizations of key data performance trends
• Automated processes to analyze and report on project results, enabling stakeholders to view up-to-date KPIs in real-time
• Implemented new analytical methodologies and machine learning models to optimize data analysis on large datasets
• Enabled secure data access to over 50 stakeholders across corporate departments, increasing collaboration between teams
• Developed an intelligent BI system for predictive analytics, improving the accuracy of data predictions by 45%
• Predictive Analytics
• Data Manipulation
• Dashboard Design
• Data Quality & Governance
• Structured Data
• AI & Automation
• Data Security & Accessibility
• Multivariate Analysis
• Data Warehousing
• Database Design & Architecture
• Big Data Analytics

Senior Data Scientist Resume Example:

• Spearheaded the creation of an advanced predictive model to forecast customer trends, producing an 8% increase in accuracy from previous models and driving a 15% growth in overall revenue.
• Developed features from raw data gathered from multiple sources and utilized BI technologies, big data, and machine learning techniques to improve data modeling results.
• Led a team of 5 junior data scientists in developing an innovative research and development pipeline, resulting in an increase of 10% in the company's product offering accuracy.
• Redesigned existing data models in order to achieve a 10% increase in accuracy and a 5% cost savings
• Collaborated with engineers and software developers to deploy newly created models into production, achieving a 40% decrease in the time to market
• Employed neural networks, decision trees, and deep learning algorithms to generate predictive models that resulted in a 25% increase in target user engagement
• Authored an effective iteration of the company’s customer acquisition strategy that increased inbound leads by 30%
• Leveraged structured and unstructured data to analyze customer behavior, identifying insights that led to a 25% decrease in customer churn
• Produced features from raw data and created visualizations to support executive decisions; resulted in a 20% increase in the team’s success rate
• Statistical modeling
• Natural language processing
• Neural networks
• Deep learning algorithms
• Business intelligence
• Data wrangling
• Feature engineering
• Generative algorithms
• Predictive modeling
• Data analysis
• Pattern recognition
• Probabilistic reasoning
• Model deployment
• Research and development pipeline management
• UI/UX development
• Database optimization
• Data engineering
• Cloud computing

High Level Resume Tips for Data Scientists:

Here are some tips to help Data Scientists get into the right mindset for the resume creation process: Highlight your data-driven mindset: Data scientists are highly analytical thinkers, so you want your resume to showcase your ability to launch data-driven projects and initiatives. Use specific numbers and results to demonstrate the impact of your work. Emphasize quantitative skills: Data Scientists have a strong set of quantitative skills, so make sure to prioritize these when crafting your resume. Highlight your experience with quantitative analytics, statistical modeling, Machine Learning, and data mining. Know your technical skill set: Showcase your technical skillset, such as experience with programming languages, databases and frameworks associated with data science. Also, list any certifications you have or software you’re proficient in. Focus on business objectives: Your data science role is about much more than simply crunching numbers. Use your resume to showcase your ability to identify business objectives and effectively translate them into data-driven projects. Tailor your resume to the job and company: Customize your resume to each job you apply for, emphasizing the skills and experiences that make you the perfect fit for the specific role and company. This can help you stand out from the competition.

Must-Have Information for a Data Scientist Resume:

Here are the essential sections that should exist in a data scientist resume:

• Contact Information
• Resume Headline
• Resume Summary or Objective
• Work Experience & Achievements
• Skills & Competencies

Additionally, if you're eager to make an impression and gain an edge over other data scientist candidates, you may want to consider adding in these sections:

• Certifications/Training

Let's start with resume headlines.

Why Resume Headlines & Titles are Important for Data Scientists:

Data scientist resume headline examples:, strong headlines.

Experienced Data Scientist with 4+ Years of Machine Learning and Knowledge Science expertise

Accomplished Data Scientist demonstrated success in Statistical Modelling and Artificial Intelligence

The good headlines provide concrete and relevant details about the Data Scientist’s experience, qualifications, and accomplishments.

They help clearly distinguish the applicant from other Data Scientists who may be applying for the same job.

Weak Headlines

Highly Skilled Data Scientist

Data Scientist looking for a new challenge

The bad headlines are too broad and don’t give any concrete information about the candidate. They also don’t demonstrate any professional or academic achievements.

Writing an Exceptional Data Scientist Resume Summary:

A resume summary is a critical component of a Data Scientist's resume, providing a succinct overview of their skills, experience, and accomplishments in the field. As a Data Scientist, your summary should emphasize your expertise in data analysis, modeling, and machine learning, as well as your ability to extract insights from complex data sets and communicate findings to stakeholders.

Here are a few tips for writing an effective summary for a Data Scientist:

• Tailor the summary to the specific job you are applying for by highlighting the most relevant skills and experiences.
• Include quantifiable achievements, such as improving predictive accuracy, increasing revenue through data-driven decision making, or implementing new data-driven processes.
• Use relevant technical terms and keywords to show your proficiency in the field and to make your resume stand out to both humans and applicant tracking systems (ATS).
• Keep the summary concise and to-the-point, around 4 sentences or less.
• Avoid using technical jargon that might be difficult for non-technical readers to understand.

Data Scientist Resume Summary Examples:

Strong summaries.

• Experienced Data Scientist with 6+ years of experience in developing and deploying predictive models for a variety of industries. Skilled in data analysis, machine learning, and statistical modeling to drive insights from complex datasets.
• Proactive and detail-oriented Data Scientist with 6+ years of experience in leveraging data to develop analytical insights for business decision making. Adept at programming in Python and R, and utilizing various data visualization tools to communicate findings.

Why these are strong:

• Both summaries are concise, feature the required experience, and provide specific examples of skills and expertise. This provides the reader with a clear understanding of the Data Scientist's abilities and experience.

Weak Summaries

• Experienced Data Scientist with 6+ years of experience. Proficient in data analysis, machine learning, and statistical modeling.
• Data Scientist with 6+ years of experience. Skilled in analytics and data visualization.

Why these are weak:

• These summaries are too vague and lack detail. They do not provide any concrete examples of the Data Scientist's experience or abilities, which would give the reader a better sense of their qualifications.

Resume Objective Examples for Data Scientists:

Strong objectives.

To leverage 2 years of versatile experience, including implementing machine learning algorithms and coding in Python, to contribute to a data science team that supports innovative solutions.

To leverage strong analytical and technical abilities to develop effective data models, visualize data, and uncover insights that drive organizational success.

• What makes the great objectives great is that they concisely emphasize the candidate's experience, technical knowledge, and desire to use their skills to contribute to organizational success.

Weak Objectives

To use my education and experience to help generate profits.

To bring my 3 years of experience in data science to a successful or growing organization.

• These resume objectives are weak because they don't effectively demonstrate the technical knowledge and experience of the candidate. The first objective does not adequately communicate the skills that the candidate has to offer. The second does not indicate how the candidate will drive value for the company.

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• Share detailed yet succinct descriptions of accomplishments and work experience. Demonstrate how you have used data science to make an impact in the organization, such as in increasing revenue or reducing costs.
• Highlight data-driven methodologies you have employed, such as machine learning, artificial intelligence, big data, and statistical analysis.
• Include a separate section for project highlights and highlight the most notable projects that you have worked on, such as successful predictive analytics projects.
• Demonstrate expertise in troubleshooting and debugging systems, as well as software engineering, if relevant.
• Showcase your collaborative capabilities by highlighting those projects you have initiated and those you have worked on with teams.
• Mention your communication skills by citing situations where you have led data science presentations, organized workshops, and authored reports or white papers.
• Illustrate the extent of your knowledge and experience with programming languages, software packages, and tools used in data science.
• Detail your experience in data warehousing and deployment, as well as data visualization processes.
• Demonstrate your business acumen by emphasizing the successes you have achieved that connected data science solutions with research and development projects, set goals, and improved customer satisfaction.

Example Work Experiences for Data Scientists:

Strong experiences.

Developed and deployed machine learning models that enabled a healthcare company to predict which patients were at high risk of hospital readmission, resulting in a 15% reduction in readmission rates.

Designed and implemented A/B tests that evaluated the impact of different product features on user engagement and revenue, leading to a 20% increase in revenue for a fintech startup.

Conducted exploratory data analysis and developed visualizations that identified key trends and insights in customer data, resulting in data-driven recommendations for improving customer experience.

Developed and implemented a deep learning algorithm that achieved state-of-the-art accuracy on a computer vision task, resulting in a publication in a top-tier conference.

Led a team of data scientists and engineers to develop and deploy a scalable recommendation system for a large e-commerce platform, resulting in a 10% increase in user engagement and revenue.

Conducted statistical analyses and designed experiments to evaluate the effectiveness of marketing campaigns, resulting in data-driven recommendations for improving campaign performance.

• These work experiences are strong because they provide specific and quantifiable examples of the Data Scientist's contributions and impact in previous roles. They demonstrate the individual's technical expertise and ability to solve complex problems, as well as their ability to communicate findings and recommendations to stakeholders. Additionally, they highlight the individual's leadership and collaboration skills, which are important for senior-level positions.

Weak Experiences

Conducted analyses on company data and presented findings to the executive team

Collaborated with stakeholders to identify business needs and develop data-driven solutions

Developed models to analyze customer behavior and recommend strategies for improving customer engagement

Cleaned and pre-processed data for analysis

Developed machine learning models for predicting customer behavior and tested model accuracy

Visualized data and presented insights to stakeholders

• The first weak work experience is too general and lacks specific details about the data analyzed, the techniques used, and the impact of the analyses. It also does not demonstrate the candidate's ability to work with complex data sets or communicate findings effectively. The second weak work experience also lacks specific details about the data analyzed, the techniques used, and the impact of the analyses. It also does not demonstrate the candidate's ability to collaborate with stakeholders or develop effective data-driven solutions.

Top Skills & Keywords for Data Scientist Resumes:

Top hard & soft skills for data scientists, hard skills.

• Computer Programming
• Machine Learning Algorithms

Soft Skills

• Problem-solving
• Critical Thinking
• Interpersonal Skills
• Adaptability
• Presentation Skills
• Written and Verbal Communication
• Organization

Go Above & Beyond with a Data Scientist Cover Letter

Data scientist cover letter example: (based on resume).

Dear Hiring Manager, I am excited to apply for the Data Scientist position at [Company]. With my extensive experience in developing and implementing machine learning models, collaborating with cross-functional teams, and leading a team of data scientists, I am confident that I have the skills and expertise needed to drive successful data-driven solutions for your company. At my previous position, I developed and implemented machine learning models to improve customer retention, resulting in a 15% increase in customer retention. I also collaborated with cross-functional teams to develop predictive models to improve business outcomes, resulting in a 20% increase in revenue. Leading a team of 3 data scientists, I was able to drive successful data-driven solutions to improve business outcomes. In addition to my technical skills, I am a proactive problem solver and excellent communicator. My ability to identify patterns and trends in customer behavior through data analysis, and develop natural language processing models to improve customer service interactions, resulted in a 15% reduction in customer complaints. As a data scientist, I have experience in conducting data cleaning and preparation tasks, and collaborating with data engineers to develop data pipelines to improve data quality and accessibility. My expertise in these areas will allow me to efficiently and effectively contribute to your team. Thank you for for reviewing my resume and considering my application for the Data Scientist position at [Company]. I am excited at the prospect of contributing my skills and expertise to your team and look forward to discussing my application with you further. ‍

Sincerely, [Your Name]

A cover letter is a valuable tool for any job seeker, and this is especially true for data scientists. Data science is a highly competitive field, and a cover letter can help you stand out from other applicants. It can showcase your communication skills, highlight your relevant experience, and demonstrate your enthusiasm for the position.

While a resume provides a summary of your skills and experience, a cover letter allows you to personalize your application and connect with the hiring manager on a deeper level. It's an opportunity to tell your story, explain why you're passionate about data science, and show how you can add value to the organization.

Here are some of the key reasons for pairing your data scientist resume with a cover letter:

• It demonstrates your communication skills: As a data scientist, communication is key. Your cover letter provides an opportunity to showcase your ability to write clearly and concisely, and to convey your ideas effectively.
• It shows your enthusiasm for the position: A well-written cover letter can demonstrate your passion for the role and the organization. This can make a big difference in the hiring manager's decision-making process.
• It highlights your relevant experience: Your cover letter allows you to explain how your skills and experience align with the requirements of the job. This can help the hiring manager understand why you're a good fit for the role.
• It sets you apart from other applicants: A well-crafted cover letter can help you stand out from other applicants who may have similar experience and qualifications.

We understand that writing a cover letter may seem daunting, but it doesn't have to be. Remember that the cover letter is an extension of your resume, so you can use the same format and content as your resume. It's also a chance to address any gaps or questions that the hiring manager may have after reading your resume.

Tips for aligning your cover letter with your resume:

• Use the same header as your resume: This will help the hiring manager identify your application as a complete package.
• Align the content of your cover letter with the requirements of the job: Use the job description as a guide to highlight your relevant skills and experience.
• Use keywords from the job posting: Incorporate relevant keywords from the job posting to help your application get past applicant tracking systems (ATS).
• Keep your cover letter concise and focused: Aim for one page and avoid repeating information from your resume.
• Proofread carefully: Errors in your cover letter can undermine your credibility, so make sure to proofread carefully before submitting your application.

Resume FAQs for Data Scientists:

How long should i make my data scientist resume.

When crafting a resume for a Data Scientist, it's important to keep it concise, concisely highlighting the most important and relevant skills, education, and experience. A general rule of thumb is to keep a resume one page in length, maximum two if absolutely necessary. Ideally, keep each section short and to the point, avoiding lengthy, excessive detail. Remember, Data Scientists should focus on creating a succinct, impactful resume that demonstrates their qualifications and value.

What is the best way to format a Data Scientist resume?

The best way to format a Data Scientist resume is to create sections for Summary, Technical Skills, and Work History/Projects. Within each section, organize bullet points with succinct, descriptive language that highlights relevant achievements. Use a simple, elegant font and structure the document for easy skimming. Include contact information and a professional headshot at the top for a polished look.

Which Data Scientist skills are most important to highlight in a resume?

Data Scientists should include the following hard skills in their resume: 1. Programming: Data Scientists should have strong knowledge in programming languages like Python, R, Java and C++. They should be highly proficient in scripting and they should have experience in a variety of databases like Mysql, MongoDB, Spark, and Hadoop. 2. Data Analysis: Data Scientists should demonstrate expertise in data analysis, data mining, machine learning and statistical modeling. They should have experience in performing exploratory data analysis, interpreting data patterns and building predictive models. 3. Data Visualization: Data Scientists should have strong knowledge in data visualization and be able to create visually appealing and interactive data visualizations using tools like Tableau, PowerBI and D3.js. 4. Communication: Data Scientists should be able to effectively communicate complex ideas to both technical and non-technical audiences and present data-driven solutions in a clear and concise manner. 5. System Engineering: Data Scientists should possess a basic understanding of system engineering, including the ability to setup and maintain complex data pipelines and ETL processes.

How should you write a resume if you have no experience as a Data Scientist?

If you have no experience as a Data Scientist, focus on articulating the skills, qualities and relevant education that make you an ideal candidate. Highlight transferable skills you've developed in any prior work or academic experience that demonstrates your aptitude for working in the field. Also emphasize any relevant projects you've completed that demonstrate your analytical abilities. You can discuss membership in organizations that are related to data science, or any certificates you have earned in data-related fields. Finally, be sure to include the technical details that reflect your understanding of languages and databases commonly used in data science roles.

Compare Your Data Scientist Resume to a Job Description:

• Identify opportunities to further tailor your resume to the Data Scientist job
• Improve your keyword usage to align your experience and skills with the position
• Uncover and address potential gaps in your resume that may be important to the hiring manager

Related Resumes for Data Scientists:

Data science fresher resume example, data science intern resume example, entry level data scientist resume example, junior data scientist resume example, senior data scientist resume example, data scientist resume example, more resume guidance:.

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Data science is one of the most revolutionary technologies helping businesses grow rapidly worldwide.

More than 105,980 data scientists are currently working in the United States at an average annual salary of $108,660.

These statistics might fascinate you to become a data scientist in 2023, and you must start learning that. However, you will need a professional data science resume after learning data science by any medium.

In this blog, we will elaborate on all the essential points to build an impeccable data scientist resume.

Table of Content:

• What does a data scientist do?
• What are the resume formats to use in a data scientist resume?
• How to write the data scientist resume header section?
• How to create a professional summary for data scientist resume
• How to add key skills in the data scientist resume?
• How to write a professional experience section of the data scientist resume?
• What is the best way to add your education experience to the data scientist resume
• How to add certification to the data scientist resume?
• How to add the additional experience to the data scientist resume?

What does a Data Scientist Do?

The primary job of a data scientist is to crunch raw data and turn them into meaningful insights that an organization needs to make valuable business decisions.

They are also responsible for developing data modeling processes & creating statistical models to crunch business data and analyze it to find meaningful insights.

Here are the typical roles and responsibilities of a Data Scientist :

• Coordinate with the stakeholders & understand the business requirements
• Acquire data from the clients & sort the data based on various criteria
• Integrate data from different data points to initiate data investigation
• Apply data science techniques including statistical analysis, AI, and machine learning to analyze data
• Use Data Visualization techniques to measure results and present results to the stakeholders.

Data Scientist Salary

Robert Half Technology’s 2020 Salary Guide says that the average salary of a data scientist is between $105,750 and $180,250 per year in the USA.

However, the salary ranges from place to place.

Here are Data Scientist Salary for cities in the USA:

• San Francisco: $121,836
• Seattle: $108,399
• New York: $101,387
• Boston: $101,064
• Los Angeles: $99,014
• Austin: $96,495
• Atlanta: $91,049
• Washington, D.C.: $89,738
• Chicago: $88,758
• Charlotte: $87,306

Best Resume Format for a Data Scientist Resume

When it comes to a data science resume format, what all options do you have?

You have three. Let's talk about the pros and cons of each.

Reverse Chronological Data Scientist Resume Format

The most conventional resume format in the market, and a darling of the recruiters, the reverse chronological resume format is the best bet for a resume of a data scientist.

As the name suggests, this resume format entails listing all your experiences and qualifications in reverse chronological order. This means listing out your most recent qualification/experience first, followed by the previous ones, until you reach your trajectory's beginning.

Sections in a Reverse Chronological Resume Format

A data analyst resume in a reverse chronological resume format contains the following sections:

• Personal Details
• Professional Summary
• Technical Skills
• Professional Experience
• Certification, Conferences, and Publications
• Additional Information

It's always better to keep the target profile in mind when you're sending across your data scientist resumes.

Also Read: What are the different sections to add to a resume? .

Pros of a Reverse Chronological Resume Format

Given below are some of the pros of a reverse-chronological resume format.

1. ATS Optimized

The reverse chronological resume format is ATS-friendly and is the industry standard for resumes. This format will guarantee that an ATS effectively parses your resume.

2. Preferred by Recruiters

Since the ATS can effectively parse resumes in the reverse-chronological format, it is preferred by recruiters as well. This format allows the hiring managers to quickly scan the resume for information they think is most relevant, thus saving valuable time.

3. Ease of Preparation

If you are a data scientist making your resume for the first time, or if resume writing is not your most preferred way of spending time (trust us, we understand!), you can simply opt for a reverse chronological resume format. You need to create a timeline of your professional trajectory and expand each item in your resume.

4. Prioritizing your most recent work experience

This format allows you to emphasize your most recent work profile. Consequently, your earlier roles (unrelated to your target profile) will be sidelined in place of your most recent professional profiles.

Cons of a Reverse Chronological Resume Format

Given below are some of the cons of a reverse-chronological resume format.

1. Gaps in your career

If there are gaps in the professional trajectory, this resume format will make it evident to the recruiter. Since the dates are mentioned for every qualification and experience, not only will the recruiter notice that, but the ATS might also disqualify you (in case you lack the required work experience).

2. Frequent job switches

If there are a lot of companies across a limited time frame, this might signal that you are an unstable employee who might not stick around for long.

Functional Resume Format for Data Scientist Resumes

At the other end of the spectrum lies the functional resume format . Here, only the professional headers are mentioned, without diving into the details of each work profile.

Instead, a separate section is created along with the Summary of Skills, wherein the relevant points are grouped under the appropriate skills.

This resume format is ideal for those with many gaps in their professional trajectory or those who have a history of frequently switching entry-level data science jobs.

Following are the pros and cons of using the functional resume format:

Pros of a Functional Resume Format

Given below are some of the pros of a Functional resume format.

1. Covers employment gaps

Since this resume format focuses on skills over the actual professional trajectory, this format is suitable for those professionals who have a lot of gaps in their careers.

2. Addresses the issue of job switches

The emphasis on skills allows the applicant to focus on skills, thus allowing the recruiter to gloss over the factor of frequently switching jobs.

Cons of a Functional Resume Format

Given below are some of the cons of a Functional resume format.

The functional resume format is not:

• ATS-friendly
• Suspicious to recruiters
• It has no focus on the career trajectory

Combination (Hybrid) Data Science Resume Format

The combination resume format or the hybrid resume format combines both reverse chronological resume and the functional resume.

The first half of the combination resume is written using the functional format. So, the first half focuses on and highlights a person's skills and accomplishments.

The second, the combination resume, is written in the reverse chronological order, which means the professional history of a person is written in the reverse chronological order.

• Provides the information in reverse chronological order
• Highlights the skills
• It Consume more time to create
• The functional resume may not comply with the company-specified guidelines

Also Read: What format to use in a resume in 2023? .

3 Stages of Writing a Data Scientist Resume

We've divided the data science resume into three stages to make the data scientist resume-making process more straightforward. These three stages are:

• Master Data Scientist Resume
• First Draft Data Scientist Resume
• Final Data Science Resume

Also Read: How to write a perfect resume header in 2023?

Step 1: Create a Master Data Scientist Resume:

The first step is to create a master data scientist resume that includes all of your relevant skills and experience from previous jobs and internships.

For example, if you have extensive experience in one field, it may be difficult to recall all important skills and achievements when you create the final data scientist resume.

So, it's better to create a Master resume, which will consiste all your professional experience and achievements in reverse-chronological order.

Then, use this template to create versions tailored for each job application at hand. This way, you'll spend less time updating your resume every time you apply for a new position — and more time actually getting interviews!

First Draft for your Data Scientist Resume:

Next, create the first draft of your data scientist resume by focusing on the job requirements of the job you're applying for.

For example, if you are applying for a business analyst role at ABC Inc., make sure to add relevant skills and experience from your master resume to the first draft resume, and leave off the unnessary skills and experiences.

In this stage, you will make the sections of the header, personal information, title, education, certifications, conferences, publications, and the section of additional information.

Final Data Scientist Resume:

In this stage, you need to do the following three things:

First, draft the skills section .

• Pick out all the skills that you acquire from your professional experience section and the job description, then write them in the key skills section of your data scientist resume.

Second, compose your data scientist resume summary/data scientist resume objective section .

• Look for the points that highlight your career in your professional experience section and write them in the data scientist resume summary/data scientist resume objective section of your data science resume by rephrasing them a little.
• Highlight and bold all the essential words, phrases, and numbers in the sections of professional experience, certifications, conferences, publications, and the additional information section.

Also Read: How to becoma a Data Scientist in 2023?

Writing Data Science Resume Header

A header comes at the top of your data scientist resume.

It consists of four important elements:

• Resume title
• Contact Information
• LinkedIn or any social media profile link(optional)
• Profile Designation

Data Science Resume Title

Given below are some tips to write the Resume Title:

• Do not write "CV" or "Resume" as the resume title. Instead use your full-name as the resume title.
• Give a single space between the first and the last name
• Keep the font size between 16-18 points when writing the title of the resume
• If you have a middle name, write the initial of your middle name on resume title. For example: if your name is Sheldon Lee Cooper, write Sheldon L. Cooper in the resume title.

See the data scientist resume sample given below to see how an on-point data science resume title should look like:

Personal Mobile Number in Data Science Resume

Write only one mobile number on which you are available 24x7.

The two rules that you need to follow while writing your mobile number are:

• Write your country's International Subscriber Dialing (ISD) code followed by a plus sign (+) before your mobile number.
• Give a single space after the first five digits of your mobile number.

Writing only that number on which you are 24x7 available is essential as a personal mobile number is a primary source through which a recruiter is likely to contact you.

Personal E-mail ID in Data Science Resume

Write only that e-mail ID in your information section, which you often use if you have multiple e-mail IDs. And keep the email ID formal and professiona.

• john.doe@email[dot]com
• doe.john@email[dot]com
• john.doe.22@email[dot]com
• doe.john.21@email[dot]com
• rockingjohn1992@email[dot]com
• lannisterfanboy101@email[dot]com

Current Location in Data Science Resume

• This is the third thing to include in the resume heading section. Here you will write the current location of your residence.
• You do not need to get into the details of your residence. For instance, No need to mention the exact address of your home.
• If you're applying for a job in your own country, write the location in City/State Code format.
• If you're applying for a job in another country, write the location in city/country code format

Add Relevant Links

At this section, you can add hyperlinks to your social media websites like LinkedIn, Facebook, Instagram, etc., and personal websites or portfolios if you have any if they provide any support for the professional work that you have done so far.

If you have live links to your projects, or Github repository, you can add those in your resume to help the recruiters understand your work experience.

Take a brief look at the data scientist resume sample below to get a better understanding of how to write the personal information section:

Also Read: How to write the contact information in resume in 2023?

Data Scientist Resume Profile Title

Next in line comes the profile title section.

The profile title tells the recruiter the level at which you carry out your job responsibilities and duties.

The title is supposed to be the second-largest text in your data scientist resume, written in between the font sizes of 12-14 points .

Writing a profile title makes the recruiter's job easy and is a deciding factor for the recruiter to read your data science resume further or not.

Look at the data scientist resume sample given below to get a better idea about how a data science resume profile title looks like:

Data Scientist Resume Skills

Writing the skills section in the final draft stage will give you more skills to write in your data scientist resume than you thought you had. Before writing your skills section, scan the rest of your data science resume once, thoroughly to look for skills.

Look at the below-given data science resume example to see how to write a key skills section:

Technical Skills Section of Data Scientist Resume

If you have skills like that of SciKit-Learn, NumPy, or SciPy, then do not include them in the key skills section. These are considered to be technical skills, so make a separate section for them named 'Technical Skills Section.' It will be made in the same way that you have made your key skills section.

List of Skills to Add for a Data Scientist

To get a better idea, take a glance at the data scientist resume sample given below:

Also Read: How to write the key skills section of a resume in 2023?

Data Scientist Resume Summary

Your recruiter resume summary is where you can clearly communicate the value that you bring to a company and what you have achieved in previous roles.

Here are some tips to write the resume summary section of data scientist resume:

• If you have 3+ years of experience, then only add the summary in the resume.
• Keep the resume summary betwieen 2-3 lines.
• Add the most important and relevant skill in the resume summary, just to get the recruiter's attention.

Look at the data scientist resume sample below to see how to optimize your data scientist resume summary section:

Also Read: How to write a resume summary in 2023?

Data Scientist Resume Professional Experience Section

A professional experience section includes all the professional work that one has done in their career.

This section holds a lot of importance for professionals with extensive career history.

You can mention the following details while framing the professional experience section of your data science resume:

• Profile Name
• Company Name
• Company Location
• Serving Period
• Highlights of Your Activities

Data Scientist Resume Professional Experience Example

Data Visualization & Predictive Analytics Steering rapid model creation in Python using Pandas , NumPy , SciKit-Learn & plot.ly for data visualization Creating NLP models for Sentiment Analysis & MapReduce modules for predictive analytics in Hadoop on AWS Deploying ridge regression model & LASSO solver via gradient descent to select the regularization parameters Statistical Modeling & ML Algorithms Applied various machine learning techniques to build dynamic pricing models and maximize profitability Led the development of a performance assessment & pricing analysis platform by deploying k-NN Algorithm Created multivariate regression based attribution models using ad stock analysis from the digital marketing data

What to Do If You Don't Have Professional Experience in Data Science?

If you don't have professional experience in data science, you may be wondering if it's possible to land a job without having a background. The answer is yes, but it's not going to be easy.

Regardless if you have experience or not, recruiters will want to see that you have the skills to become a data scientist that they can trust.

So, it's extreamly important that you've done some work in the field.

If you alreay have some works in data science filed, add these works in your resume.

If you don't have any experience yet, here's some dea you can start with.

1. Do Some Personal Projects

This could be anything from looking at your own spending habits to analyzing popular products on Amazon or Reddit.

You can also look at open source datasets online and try building models with them or find other interesting projects to try out on your own. These personal projects will give you hands-on practice with machine learning and help build up your portfolio.

2. Get Involved in Kaggle Competitions

Participate in data science competitions online, like Hackathons and Data Science Competitions (e.g. DSC). These are great ways to gain skills and aexposure because they're judged based on algorithms rather than coding skills.

And participating certification of these competitions are extreamly valuable when applying for a job.

Action Verbs for Data Scientist Resume

Here are some of the action verbs you can use in the data scientist resume:

• Demonstrated
• Initialized
• Spearheaded
• Conceptualized

Look at the data science resume example below to see how a professional experience section should look like:

Also Read: How to write the work experience section of a resume in 2023?

Data Scientist Resume Internship Section

While still in college, students are likely to do a maximum of two internships. These internships help you gain professional experience and hands-on professional knowledge of the workings of your chosen field. So, that is why the internship section holds a lot of importance in a freshers data analyst resume.

Including internships in your data scientist resume will give you the upper hand over the freshers who haven't done any internships during their graduation.

Use the following format to write your internship section:

{Name of the Organization} | {Location} (city, country pin) | {Dates} (in mm/yy-mm/yy format) | {Designation}

This section of the internship will also be written using the cause-effect method along with grouping & highlighting, as explained in the professional experience section above.

Also Read: What are the popular Data Science career options in 2023?

Education Section in the resume of a Data Scientist

The education section is one of the essential sections of a recruiter's data science resume. It helps the recruiter decide whether you are eligible for the job.

Mentioned below are the elements you need to include in the education section of a data scientist:

• Name of the university you have attended
• Name of the courses you have pursued
• Location of your school
• Enrollment and graduation dates in the month and year format

The data scientist resume sample provided below will give you a better idea of how to write the education section:

Also Read: How to write the education section of a resume in 2023?

Certification Section in the Resume of a Data Scientist

The second last section in the first draft stage is the certifications, conferences, and publications section.

In this section, you will write all the certifications, conferences, and publications that you ever got, attended or got published. Also, these should be those certifications, discussions, and magazines that will add some value to your data scientist resume.

Use the format given below to write your certifications.

{Name of Certification} | {Affiliating Institution} | {Location} | {Date (month & year)}

To write your conferences, use the below-given format:

{Your Role in the Conference} | {Title or Topic of Discussion} | {Conference/Forum Name} | {Date (in mm/yy) and Location}

Use the format given below for writing your publications:

{Authors Name} | {Title of Article/ Chapter} {Name of journal, website, magazine, etc.} | {Year of Publication} | {Publisher or Issue Number} | {URL if its an online publication}

See the below-given data scientist resume example to see how the certifications, conferences and publications section should be like:

Also Read: How to write the certification section on a resume in 2023?

Data Scientist Additional Information Section

The last section to be made in the first draft stage is the additional information section.

In this section, you will write additional information about the languages you know. For example, if you are fluent in speaking and writing English and Spanish, you will mention it in this section.

Also, you will make this section of additional information only if you know how to speak and write more than one language.

To get a more clear idea on how to write this section, look at the data scientist resume sample given below:

Data Scientist Resume Sample

If you follow all of our tips, you can achieve an industry-standard data science resume like the data scientist resume sample given here!

• Packages: scikit-learn, NumPy, SciPy, Plot.ly, pandas, NLTK, Beautiful Soup, Matplotlib, Stats Models
• Big Data Stack: Hadoop, Apache, Pig, Python, PostgreSQL, AWS, Hive, MongoDB, MapReduce, Spark, Linux
• Statics & Machine Learning: Liner & Logistic Regression, SVM, Ensemble Trees, Random Forests, Gradient Boosted trees
• Received Best Employee Award in the year in '20 for showcasing due diligence for work
• Established the data science division from scratch by recruiting, on-boarding & training a team of 8 data analysts
• Formulated clustering & regression analysis to resolve a shipping consolidation issue & reduce costs by USD 3 million
• Migrated data transformation processes on Hadoop to reduce data processing time by 25% & cut costs by USD 550k
• Developed a customer segmentation algorithm via Python to boost sales leads & increase market share by 28%
• Steered rapid model creation in Python via Pandas, NumPy, scikit-learn & plot.ly for data visualization
• Constituted NLP models for Sentiment Analysis & MapReduce modules for predictive analytics in Hadoop on AWS
• Designed real-time contextual behavioral personalization system via econometric & ML to predict customer behavior
• Placed various machine learning techniques to build dynamic pricing models and maximize profitability
• Led the development of performance assessment & pricing analysis platform via k-NN Algorithm
• Formed multivariate regression based attribution models via ad stock analysis from the digital marketing data
• Generated segmentation models using K-means Clustering in order to discover new segment of users
• Accorded with CEO Appreciation Award for outstanding performance in the year '18
• Performed a key role in yielding a K-S static of 51.5 by developing a logistic regression model and an additive scoring model for QSM
• Deployed SGD, Logistic Regression, Random Forest, SVM, etc. for classification models to boost average click rate by 34%
• Applied large scale & low latency machine learning for non parametric models & high-dimensional data visualization
• Utilized high-dimensional data sets from users, media agencies & 3rd party apps via PCA, LDA & Kernel Approximations
• Created multivariate regression-based attribution models & segmentation models via K-means Clustering
• Directed model development, validation, testing and implementation of analytical products and applications
• Stationed advanced text mining algorithms to identify search intent latent in individual keywords
• Tested and implemented decision trees, random forests, and ensemble model via bagging and boosting
• Employed Principle Component Analysis to analyze collinearity , and reduce the dimensionality of the dataset
• CGPA: 3.8/4.0
• Certified Machine Learning Expert | OpenAI | Aug '17
• Certified Expert Data Scientist | Stanford University | Jun '16
• Speaker | Open Data Science Conference | SF, CA | Jan '16
• Published - Modern Method of Dynamic Pricing for Hotels | The Data Science Journal | May '15
• Volunteer at Home Shelter| Jun '20 - Present
• Member of Boys & Girls Club | Organized workshops on importance of education | Jun '13 - Aug '18
• Languages: English (native), Spanish (working proficiency), and Catalan (fluent)

Key Takeaways

You can build a data science resume by referring to any online data science resume example. However, you might face issues without actionable steps and expert guidelines.

Take a look at the essential points to remember while building your data scientist resume in 2023:

Ensure that your data scientist resume summary does not exceed the limit of 3-4 lines.

Mention only those skills and achievements which really make you stand out from the other applicants.

Tailor your professional experience section according to the job description.

Make the key skills section just after your data scientist resume summary , followed by the technical skills section.

Add profile-centric keywords picked from the job description to parse through the ATS.

These guidelines will help you craft an effective data scientist resume. In addition, you can check out Hiration's Online Resume Builder to minimize your resume building time.

This is an AI-powered platform with 24/7 chat support to offer you a smooth operating experience. You can also leverage our career assistance at [email protected] .

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• Data scientist objectives and summaries

Data scientist Objectives & summaries

14 Data scientist objectives and summaries found

A well-written objective or summary on your resume can be the difference between getting rejected, or getting invited for an interview. Copy any of these Data scientist objective or summary examples, and use it as inspiration for your own resume. All examples are written by certified resume experts, and free for personal use.

Learn more about: objective vs. summary

Data scientist resume summaries

Detail-oriented and high-energy individual with strong planning and organizational skills. Experience working under lead data scientist and other team members to create and implement scalable cloud-based data analytic solutions in fast-paced environments with changing priorities. Capable of applying the latest technologies and strategies in data mining and predictive analytics to derive value from disparate data sets and events.

Data-driven, analytical Data Scientist with extensive experience in Artificial Intelligence (AI). Builds, trains, and deploys machine learning models. Provides in-depth analysis, discovers root causes, and designs long-term solutions. Expertise in terabyte size datasets, examining large amounts of data to discover hidden patterns, using data visualization tools. Proven track record of dealing with ambiguity, prioritizing needs, and delivering results in a dynamic environment.

Demonstrated ability to work with scalable distributed data processing, data management, and data visualization tools including Accumulo, Hadoop, Kafka, and various graph databases. Successfully communicates complex technical information with diverse groups of people at all business levels in an easily understandable way. Utilizes effective listening skills to build strong relationships with colleagues, key stakeholders, and clients to ensure company success.

Forward-thinking Data Scientist with the ability to quickly assess business needs, define and implement long-term strategies to meet performance and profit goals. Exceptional leadership abilities to build, mentor, and galvanize teams of data analysts, software engineers, and network architects. An excellent communicator who establishes rapport across all levels of an organization. Champions a work culture built on accountability and integrity.

Recently updated technical skills as a Data Scientist at the DeVry Institute of Technology supported by 2 years of practical experience collecting, storing, and interpreting data with fintech companies. Strong organizational skills coupled with strong attention to detail to ensure high-quality and accurate project results. Vast experience using Kafka, Flink, Storm, Spark, Hadoop, and MapReduce in collaborative settings.

Hands-on experience as a Data Scientist creating data points and organizing categories to align with business processes within the healthcare industry. 10 years of experience translates to effective data-based solutions in a variety of business facets. Exceptional skill in building rapport with cross-functional teams to identify the best strategies and practices to ensure company success in highly-competitive markets.

Solutions-focused Data Scientist with 10 years of experience. Served as interim Data Scientist Lead for a Forbes 500 fin-tech company, controlling financial data analytics, proprietary program languages, and exploration of new technology tools to streamline processes. Experienced in hiring and building teams of exceptional talent to overcome today’s business needs and challenges to grow company revenue growth and client base.

Energetic and dedicated data scientist with 15 years of leadership experience with a health insurance company, developing and implementing data analytics to facilitate internal processes and make decision-making more efficient. Progressive background includes working with various new technologies and tools, developing algorithms and writing programming languages, testing and analyzing data sets, generating reports, and presenting on complex business problems.

Data scientist resume objectives

Seeking a challenging and learning position to extract meaning from and interpret various types of data utilizing the up-to-date technologies and resources. Demonstrated ability to learn quickly and work well as part of a team or independently as needed, with effective communication across all company levels.

Highly motivated and results-driven professional seeking a Data Scientist position to accelerate IT growth through the application of a wide range of analytic methodologies and experience utilizing a variety of technologies and tools to interpret data sets of varying sizes.

Seeking a Data Scientist role at an international start-up to apply domestic and international experience and education stemming from military assignments abroad. Articulate, problem solver with superior analytical and communication skills. Brings an innate ability to encourage teamwork.

Methodical, organized, and meticulous Data Scientist seeking to position with a mid-size company to apply forward-thinking processes and exceptional team-building abilities to analyze and interpret large data sets and communicate findings clearly and concisely through effective data visualization.

Senior Data Scientist with 20+ years of experience seeking a position to combine strong expertise in technology solutions and business development. Possesses talent to work efficiently under pressure and stress in busy environments by demonstrating exceptional organization and strategic planning.

Proven ability developing, installing, maintaining, and troubleshooting data analytical systems to support business goals and objectives. Seeking to apply project management, team management, communication, client rapport, and negotiation skills to increase a company’s client base and bottom line.

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Data Analyst Resume Example & Writing Guide Examples & Writing Guide

Writing Guide for Creating an Impressive Data Analyst Resume

Here are our most important resume guidelines we recommend all job seekers follow for a significantly better shot at getting invited for an interview.

Here’s what we’re going to cover in this guide:

• How to properly format your resume for success in 2023 
• How to talk about your work experience and personal projects
• How to create a competitive skills section
• How to customize your resume for each job you apply to

Creating a data analyst resume that effectively showcases your skills and experiences is essential for standing out in today's competitive job market. As a data analyst, you have the unique opportunity to highlight your technical expertise, problem-solving abilities, and relevant work experiences to potential employers. A well-crafted resume not only increases your chances of getting noticed by hiring managers, but also demonstrates your professionalism and attention to detail.

Having a strong data analyst resume has several benefits, including:

• Focus on your achievements: Instead of just listing your job responsibilities, highlight your achievements as a data analyst. For example, you might describe how you helped a company save money by identifying inefficiencies in their data processes or how you used data to improve customer engagement.
• Quantify your impact: Use specific numbers and metrics to demonstrate the impact of your work. For example, you might describe how you increased website traffic by 20% or reduced customer churn by 15%.
• Emphasize your technical skills: As a data analyst, your technical skills are crucial. Make sure to highlight your proficiency in relevant software and programming languages, such as SQL, Python, R, or Tableau.
• Showcase your problem-solving abilities: As a data analyst, you are expected to solve complex problems. Use your resume to highlight your problem-solving skills, such as how you identified data quality issues and implemented solutions to improve data accuracy.
• Highlight your ability to communicate complex data: As a data analyst, you must be able to communicate complex data to a non-technical audience. Highlight your ability to create clear, concise reports and visualizations that communicate your findings effectively.

This guide will provide tips and best practices for creating a winning data analyst resume, so you can confidently showcase your skills and experiences to potential employers. Whether you're a seasoned professional or just starting out, this guide will provide the tools and resources you need to create a data analyst resume that will impress hiring managers and increase your chances of landing your next job in the field.

How to format a Data Analyst resume

Reverse chronological structure: Recent experience first

Reverse chronological resume is the most popular which almost all job seekers should be using. This format means listing out the most recent and relevant things first. It starts with the most recent timeline and you work your way backward, e.g. the latest job position comes first in your work experience section, not the oldest. Structuring your resume this way makes your career timeline more established. 

Data Analyst Resume Length

Stick to 1 page.

Most Data Analysts have found that their best shot at landing an offer is by having a one or two-page resume. This includes a collection of sections that cover relevant past experiences. Your resume should be no longer than three pages. The risk of going beyond two pages is including irrelevant content that could distract from your qualifications.

Avoid Common Data Analyst Resume Format Mistakes

1) Resumes must include as much content as possible.

2) Uniform and labeled section headers

When it comes to creating an ATS-optimized resume, it’s good to know how it will be read by the software. Like most human readers, the ATS will read from left to right and top to bottom. 

3) Minimal white-space

9 Expert Business Resumes Examples To Use For Business Related Jobs(Including Writing Tips)

Key Sections for a Data Analyst Resume 

A tailored resume summary .

In a competitive field like Data Analyst, hiring managers sometimes review hundreds of applications for a single position. Writing a resume that stands out is difficult, so solve that problem by including a compelling introduction for your resume. Begin your resume with a resume objective, which is a 2–3 sentence paragraph about your job-relevant skills and career goals. Refer to a data analyst resume example for a better understanding of an effective objective.

Examples of Data analyst resume summaries

         Data Analyst with 4 years of experience in Data visualization, R, Matlab. Extensive experience in developing and maintaining data visualizations using a variety of techniques including animations and interactive visualizations. Proven record of delivering high-quality analysis reports and presentations. Applying for a position as Mid-Senior level Data Analyst at Company A.

         Data Analyst with 5 years of experience in R, Python, and SQL programming languages. Proven experience in data analysis and machine learning. Strong understanding of data visualization, R programming. Applying for Manager position to drive new strategies for data analytics and reporting.

         Data Analyst with 6 years of experience in Data Cleaning, Data Visualization, Machine Learning. Proven ability to develop, test, and maintain machine learning and predictive models for large-scale data sets. Excellent analytical, problem-solving, and communication skills. Working with the marketing team to develop a model to predict customer lifetime value.

You can read more about how to create a resume summary with artificial intelligence here.

Your Relevant Professional Experience.

Employers will want to know what you’ve done in your previous job position to get insight into your skills and expertise. Knowing what you’re familiar with will help them make a decision and to see if you’re more of a good fit compared to the other applicants.

However, you should also be including any positive results you’ve accomplished. To see what this looks like in practice, you can refer to a data analyst resume examples . This will give you a clear understanding of how to effectively communicate your achievements and showcase your value to potential employers. Additionally, a data analyst resume example can also provide inspiration and help you structure your own resume to make it stand out.

Business Certifications

Emphasize your credentials. Include mention of all Important Skills for Business Jobs , Open Certified Data Scientist, Springboard Data Analytics Certification, Certified Analytics Professional(CAP), Associate Certified Analytics Professional, Cloudera Certified Associate Data Analyst, Microsoft Certified : Data Analyst Associate, Microsoft Certified Azure Data Scientist Associate, Certification of Professional Achievement in Data Sciences or other Business methods or processes.

Data Analyst Skills to Include

We suggest adding the categorized skills section at the end of your resume. Be sure to include those that are specifically mentioned in the job ad; these are the most important “keywords” that will help rank your resume highly when it is scanned by an automated applicant tracking system during its first round of review.

Hard skills usually include transferable abilities with software, tools and or other professional resources. But don’t forget to include soft skills like teamwork and communication skills, which are necessary to coordinate with an agency, designers, marketing people, business owners and clients. 

Here are more key hard and soft skills that look great on a Data Analyst resume:

Technical skills to consider

• Business programs: Data Warehousing, Machine Learning, Database Query Languages, PSQL, PL/SQL, Programing Languages, R, Python, Data visualization, Statistical Analysis, Data Cleansing, SQL Databases 
• Industry credentials: Open Certified Data Scientist, Springboard Data Analytics Certification, Certified Analytics Professional(CAP), Associate Certified Analytics Professional, Cloudera Certified Associate Data Analyst, Microsoft Certified : Data Analyst Associate, Microsoft Certified Azure Data Scientist Associate, Certification of Professional Achievement in Data Sciences 

Soft skills to consider

• Communication
• Analytical thinking
• Attention to detail
• Product understanding

Tips for Writing a Data analyst resume

Using metrics to quantify experiences.

Don’t simply fill your resume with lists of your Data Analyst job responsibilities. To impress the hiring manager, put your experience in context with specific examples and hard numbers that prove you’re an accomplished Business Specialist.

Describe how you accomplished something from your previous corporate role. Showcasing the achievements that took place as a result of your contribution will keep the reader engaged. It’s also effective in creating a strong impression and demonstrating your level of ability by highlighting the impact you’ve made.

Without any evidence, it’s hard to trust everything that someone might say. Be factual and take an objective approach. Numbers, figures, and statistics are your best friend. These will make your statements and points a lot more credible. 

Data Analyst Resume Keywords

How to tailor your resume to a job. Many hiring managers use applicant tracking systems (ATS) to filter applications based on resume keywords they write in their job ads. The more Data Analyst-related keywords you can use, the higher the chances your resume gets past the ATS and onto the hiring manager’s shortlist of applications.

The ATS keyword research process doesn’t have to be difficult. In fact, it’s a straightforward process if you’re doing it with Rezi. Our AI Keyword Targeting feature allows you to upload a job description and instantly see which keywords should be included in your resume's content.

Instantly Generate Data Analyst Resume Content

Job seekers can now use AI to create the perfect application with minimal effort. How long does it take to make a resume writes tailored content instantly and prevents writer’s block completely. It’s that simple yet highly effective in helping you get more job offers and interviews faster. 

All you need to do is enter a few details such as:

• Experience level

Learn more about Rezi AI Resume Writer here

Improve your chances with a compelling Cover Letter

Many job seekers today overlook the importance of a cover letter . The significant impact it can leave on your recruiters will play its part in the decisive moment for whether you’re going to get hired or not.

To put it simply, data analyst cover letters can be your gateway to improving your overall job application and a higher chance of getting the job.

Unless your resume is absolutely perfect with no flaws, why settle for less? A cover letter can greatly increase your odds of getting hired for the company you want to work for, even if it’s a competitive job posting.

Learn more about Rezi AI Cover Letter Builder here

Our most Recommended resume templates

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Negative space gives readers breathing room and guides their eyes to where you want them to go. Simplicity = sophistication.

A design familiar for recruiters and hiring managers. Good for corporate positions where you’ll need to paint within the lines.

Maximizes page space for dense information. Ideal for seasoned professionals with a lot to say in a limited area.

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Rezi is an awesome ai-based resume builder that includes templates to help you design a resume that is sure to check the boxes when it comes to applicant tracking systems. this is a great jumping off point to kickstart a new resume..

Frequently Asked Questions (FAQs)

Everything you need to know about using Rezi's AI Writer.

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Rezi partnered with OpenAI to use their GPT-3 neural network. GPT-3 is an innovative and powerful new neural network. We trained it to understand best resume writing practices from our experience and database of resume content. Integrating this AI into Rezi, we are able to transfer expert-quality resume writing at almost no cost.

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• • Analyzed over 1TB of open-source datasets to identify key trends and patterns, contributing to three major public research projects.
• • Employed statistical methods to clean and prepare data, enhancing the quality of information available to community stakeholders.
• • Created compelling visualizations using Tableau that increased the comprehension of complex data sets for non-technical audiences.
• • Presented findings to a team, enhancing strategic discussions and enabling data-driven decision-making for upcoming initiatives.
• • Collaborated with cross-functional groups to align data analysis outcomes with organizational goals, resulting in optimized data resources for public use.
• • Contributed code to a civic tech project, addressing local issues and positively impacting 10,000+ residents.
• • Participated in hackathons, developing solutions for data-driven challenges faced by non-profits in San Diego area.
• • Assisted in the research and development of algorithms that improved service delivery efficiency by 15%.
• • Engaged in peer reviews and collaborative problem-solving sessions to enhance project outcomes.
• • Processed and analyzed survey data from local social programs to assess impact and efficacy.
• • Led the development of an interactive dashboard to help visualize community needs and resource distribution.
• • Organized data collection drives that increased available information for analysis by 20%.
• • Facilitated workshops to teach basic data literacy to other volunteers.

5 Data Science Intern Resume Examples & Guide for 2024

As a data science intern, your resume must highlight relevant technical skills. Showcase your proficiency with programming languages such as Python or R, and tools like SQL and Tableau. Don't forget to include your experience with statistical analysis and machine learning. Detail any projects or coursework where you have applied these methods to solve real-world problems.

All resume examples in this guide

Traditional

Resume Guide

Resume Format

No Experience?

Hard & Soft Skills

Education and Certifications

Resume Objective Tips

Additional Sections

Key Takeaways

A common resume challenge faced by a data science intern is effectively showcasing relevant projects and experience in a way that stands out amidst other applicants with similar backgrounds. Our guide provides detailed strategies for clearly articulating the impact and technical complexities of their projects, thereby enabling candidates to demonstrate their unique value proposition to potential employers.

• Incorporate data science intern job advert keywords into key sections of your resume, such as the summary, header, and experience sections;
• Quantify your experience using achievements, certificates, and more in various data science intern resume sections;
• Apply practical insights from real-life data science intern resume examples to enhance your own profile;
• Choose the most effective data science intern resume format to succeed in any evaluation process, including Applicant Tracking Systems (ATS).
• PR Intern Resume Example

Optimize your data science intern resume format to pass the ATS and recruiters' assessments

What recruiters are looking for is systematised content that is clear and coherent. Thus, your data science intern resume needs to answer requirements and why you're the best candidate for the role from the get-go.

• Sorting your experience in the reverse chronological order - starting with your most recent and relevant roles. This is an excellent choice for more experienced professionals;
• Writing your contact information (e.g. personal phone number and email address ) and your portfolio or LinkedIn link in your data science intern resume header. If you're wondering to include a photo or not, always make sure that it's appropriate for the country you're applying in;
• Use the basic, most important data science intern resume sections - your experience, education, summary, etc. Use your resume's real estate wisely to tell a compelling, professional story and match job description's keywords;
• Don't go overboard with the length of your resume. One page is absolutely fine if you happen to have under a decade of relevant experience.

Are you still wondering if you should submit your data science intern resume in PDF or Word format ? The PDF has a few more advantages, as it doesn't change the format and the text can't be altered upon application.

Format matters most when your data science intern resume is assessed by the Applicant Tracker System (or the ATS).

The ATS parses resumes, looking for specific keywords, skills or experience that match the job description.

P.S. We recently did a study on how the ATS works and were able to demystify three of the biggest misconceptions about how it assesses candidate resumes.

To pass the ATS evaluation, select any of the serif or sans-serif fonts. Popular choices that would help your data science intern resume stand out include Raleway, Exo 2, Montserrat, etc.

Most traditionalists go for Arial or Times New Roman, but it's often the case that many candidates choose these fonts, and you'd thus lose points on the uniqueness front.

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If you don't happen to have that much relevant experience for the role, you could select a different format for your resume. Popular choices include:

• functional skill-based resume format - that puts the main focus on your skills and accomplishments;
• hybrid resume format - to get the best of both worlds with your data science intern experience and skills.

Data Science Intern resume sections to answer recruiters' checklists:

• Header to help recruiters quickly allocate your contact details and have a glimpse over your most recent portfolio of work
• Summary or objective to provide an overview of your career highlights, dreams, and goals
• Experience to align with job requirements and showcase your measurable impact and accomplishments
• Skills section/-s to pinpoint your full breadth of expertise and talents as a candidate for the Data Science Intern role
• Education and certifications sections to potentially fill in any gaps in your experience and show your commitment to the industry

What recruiters want to see on your resume:

• Proficiency in data science tools and programming languages such as Python, R, SQL, and machine learning libraries.
• Evidence of practical experience with data analysis, including internships, projects, or participation in data competitions.
• Understanding of data manipulation and cleaning techniques to ensure data quality for analysis.
• Experience with data visualization tools and software such as Tableau, Power BI, or matplotlib in Python.
• Knowledge of statistical analysis, hypothesis testing, and modeling techniques relevant to data science.

Data Science Intern resume without experience: a walk-through guide

If you don't happen to have any relevant experience yet, you can substitute this with:

• Short-term gigs and stunts - like month-long internships, that you have done during your university days
• Contract work - be specific about the relevance and outcomes of each role you include
• Resume format that prioritizes your skills - the functional-skill-based format or hybrid format could work
• Research roles - feature those especially prominently if you've participated in a noteworthy project or your role was of utmost importance to the project's success.

Quantifying impact on your resume

• Detail the size and complexity of the datasets you have managed or analyzed to demonstrate your ability to handle significant data volumes.
• Include any improvements in processing time or efficiency you contributed to, showcasing your ability to optimize data operations.
• Specify the percentage by which your insights or models increased accuracy, revealing your impact on predictive analysis.
• Document any cost savings achieved through your data-driven decisions, highlighting your contribution to the company's financial health.
• List how many data visualizations or dashboards you developed, to represent your skill in data interpretation and communication.
• Mention the number of machine learning models you've built and their operational use to show real-world application of your skills.
• Quantify the outcomes of your findings or recommendations, like how much they improved a business process or metric.
• Cite the number of projects you've completed or led as it showcases your project management and leadership experience.

Action verbs for your data science intern resume

You can most certainly use color on your data science intern resume to highlight important information (e.g. headline, job titles, degrees, etc.) or add more personality (e.g. icons and different resume section headings). Just make sure you use up to two colors (primary and secondary), as too many different hues may make your data science intern resume seem too unprofessional or childish.

Balancing hard and soft skills in your data science intern resume

Recruiters indeed pay close attention to the specific hard and soft skills candidates possess. Hard skills refer to technical abilities or your proficiency in technologies, while soft skills are the personal attributes and qualities developed over your lifetime.

If you're unsure about effectively quantifying these skills on your resume, follow our step-by-step guide. It's crucial to first understand the key job requirements for the role. Doing so enables you to accurately list your:

• Hard skills in sections like skills, education, and certifications. Your technical expertise is straightforward to quantify. Most organizations find it sufficient to mention the certificates you've earned, along with your proficiency level.
• Soft skills within your experience, achievements, strengths, etc. Defining interpersonal communication traits in your resume can be challenging. Focus on showcasing the accomplishments you've achieved through these skills.

Remember, when tailoring your data science intern resume for the Applicant Tracking System (ATS), ensure that the skills you list match exactly with those in the job requirements. For instance, if the job listing specifies "Microsoft Word," include this exact term rather than just "Word" or "MSO."

Top skills for your data science intern resume:

Machine Learning

Data Analysis

Data Visualization

Statistical Analysis

Deep Learning

Big Data Technologies

Data Wrangling

Problem-Solving

Critical Thinking

Communication

Attention to Detail

Time Management

Adaptability

Persistence

Project Management

The top 5 certifications for your data science intern resume:

• Certified Analytics Professional (CAP) - INFORMS
• Data Science Certificate - Harvard University
• Data Science MicroMasters - University of California, San Diego (UCSD)
• Professional Certificate in Data Science - Microsoft
• IBM Data Science Professional Certificate - IBM

Showcase academic background with education and certifications' sections

Listing your education and certifications should be a rudimentary part of your resume writing.

Including your relevant academic background - in the form of your higher education degree and niche-specific certificates - will prove knowledge of the industry.

For your education section:

• Start by including your degree, followed by start and graduation dates, as well as the institution;
• You could include relevant coursework, major/ minor , or GPA, only if your've just graduated from college or if this information would further support your application;
• If you have an "ongoing" degree,, you can still list it in case you think your diploma can impress recruiters or it's required;

Follow a similar logic for your certifications section by listing the institution, alongside dates you've obtained the certificate. For some of the most recent and relevant industry certificates , check out the next part of our guide:

Recommended reads:

• Perfecting the Education Section on Your Resume
• How to List GPA on Your Resume

Select a professional font for your data science intern resume (like Ruby or Montserrat) to ensure it stays legible.

• How to List a Major & Minor on Your Resume (with Examples)

Which one to use: a resume summary or a resume objective?

The data science intern resume summary or objective serves as a good introduction to your experience for recruiters.

Have you ever wondered which one (the summary or objective) will be more appropriate for your data science intern resume?

• If you are a less experienced professional, write a resume objective statement. The objective is about three sentences long and provides recruiters with information about your career goals, strengths, and achievements . It should basically denote how you see yourself in this particular role, and what is your relevant experience and/or know-how;
• If you happen to have plenty of relevant experience, select your most impressive achievements for your resume summary. The summary is no longer than five sentences and serves as a storytelling instrument - highlighting your greatest career wins . Don't forget to align your summary with the job requirements to ensure your resume stays relevant to the role, while passing the Applicant Tracker System assessments.

Read on for more information and examples of resume summaries and objectives from real world professionals.

Resume summaries for a data science intern job

• To leverage advanced proficiency in Python, R, SQL, and machine learning to assist in the development and optimization of data-driven strategies that propel the analytics capabilities of an established finance institution in New York.
• Seeking to utilize strong statistical analysis and predictive modeling skills to support the customer data insights team in a leading e-commerce conglomerate to enhance decision-making processes and customer experience.
• Aspiring to contribute innovative data visualization and algorithmic problem-solving abilities to enhance data infrastructure projects in a fast-paced tech startup located in Silicon Valley.
• Eager to apply an educational foundation in data science and hands-on experience from collegiate research projects to deepen the understanding and application of big data analysis for a healthcare provider in Boston.
• Aiming to bring meticulous data curation, data management expertise, and passion for machine learning to bolster real-time data processing and analytics systems for an energy sector leader in Houston.
• Intent on bringing a skill set encompassing data mining, AI development, and complex statistical analysis to the market research team of a globally-recognized environmental consultancy firm in Berlin, aiming for breakthrough insights on sustainability trends.

More relevant sections for your data science intern resume

Perhaps you feel that your current resume could make use of a few more details that could put your expertise and personality in the spotlight.

We recommend you add some of these sections for a memorable first impression on recruiters:

• Projects - you could also feature noteworthy ones you've done in your free time;
• Awards - showcasing the impact and recognition your work has across the industry;
• Volunteering - the social causes you care the most about and the soft skills they've helped you sustain and grow;
• Personality section - hobbies, interests, favorite quote/ books, etc. could help recruiters gain an even better understanding of who you are.

Key takeaways

• Your data science intern resume is formatted professionally and creates an easy-to-read (and -understand) experience for recruiters;
• You have included all pertinent sections (header, summary/objective, experience, skills, certifications) within your data science intern resume;
• Instead of just listing your responsibilities, you've qualified them with skills and the results of your actions;
• Within your data science intern resume, you've taken the time to align specific job requirements with your unique expertise, showcasing the value you can provide as a professional;
• Technologies and personal skills are featured across different sections of your data science intern resume to achieve the perfect balance.

Looking to build your own Data Science Intern resume?

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How to Become a Data Scientist?

Author: University of North Dakota May 22, 2024

In the rapidly evolving landscape of technology and business, data science has emerged as a beacon of progress and innovation.

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Every day, our digital world expands by an astonishing 328 million terabytes of data , underscoring the relentless pace of the digital age's expansion. 

This immense volume of data is not just a number; it represents a myriad of opportunities and challenges, highlighting the critical need for adept data scientists. These professionals are at the forefront of navigating the complexities of this vast information sea, uncovering patterns, predicting trends and crafting actionable insights that drive strategic decisions and shape the prospects of industries and societies.

Are you intrigued by the prospect of becoming a data scientist? Ready to dive into the depths of data and emerge with invaluable insights? This guide is your starting point. Join us as we delve into the role and responsibilities of a data scientist, outline the essential steps to becoming a data scientist and unveil the promising job outlook and competitive salaries that await in the field of data science.

What is a Data Scientist?

Data scientists are indispensable assets in today's data-driven business environment, entrusted with managing and analyzing vast datasets to derive actionable insights. Far from being mere analysts, they possess the unique ability to transform raw data into tangible strategies that propel business success. Their role transcends data management; it involves navigating through raw data to uncover hidden patterns, trends and correlations essential for unlocking untapped business potential. 

Equipped with a rich toolkit encompassing statistical methods, programming languages and domain-specific expertise, data scientists employ sophisticated algorithms and methodologies to extract invaluable insights.

What Does a Data Scientist Do?

Data scientists have a variety of responsibilities, which include, but are not limited to:

• Collecting, cleaning and processing data to ensure it is accurate and trustworthy
• Analyzing data to find patterns, trends and irregularities
• Developing and implementing machine learning algorithms to forecast and predict data
• Creating reports and visualizations to share insights and findings with stakeholders
• Working with other teams to integrate data-driven solutions into business operations
• Continuing to learn and stay up-to-date with new technologies and methodologies in data science
• Handling data ethically and adhering to data privacy regulations to maintain confidentiality and security

Steps To Become a Data Scientist

To become a skilled data scientist, one must follow several crucial steps. From formal education to obtaining certifications and doing internships, each step helps shape one's proficiency and expertise in the field. 

Earn a Relevant Degree

Earning a degree in Data Science , Computer Science , Statistics or a related field typically serves as the first step for individuals aspiring to become proficient data scientists. These degree programs provide a student-driven curriculum designed to equip learners with the fundamental concepts, methodologies and tools essential for navigating the intricacies of data science. 

Through rigorous coursework, students begin to explore various subjects, including data analysis, statistical modeling and database management. They gain both theoretical knowledge and practical skills crucial for success in the field. Additionally, these programs often incorporate hands-on learning experiences, allowing students to apply their classroom learning to real-world scenarios. 

Hone your Data Science Skills

Developing proficiency in integral areas such as programming languages like Python or R, statistical analysis and data visualization is essential for aspiring data scientists to excel in their field. These skills are key for data science work, enabling professionals to manipulate and analyze data effectively, derive meaningful insights and communicate findings visually. 

Aspiring data scientists have a multitude of avenues to hone these skills, ranging from online courses and workshops to immersive boot camps and self-directed learning resources. These platforms offer flexible learning opportunities tailored to different learning styles and levels of expertise, allowing individuals to progress at their own pace and delve deeper into specific areas of interest.

Gain a Data Science Certification

Next, a data science certification is a valuable asset that can significantly enhance credibility and career prospects. These certifications provide tangible evidence of an individual's proficiency in various areas of data science, including data analysis, machine learning and data visualization. Moreover, certifications signify a commitment to continuous learning and professional development, reflecting positively on an individual's dedication to staying updated. 

Many reputable organizations offer certification programs tailored to specific domains or technologies within data science, allowing individuals to specialize and differentiate themselves in the competitive job market. Whether it's certification in a particular programming language, a specific machine learning framework or a specialized area of data analysis, these credentials enable individuals to showcase their expertise in niche areas, thereby increasing their marketability and competitiveness.

Some excellent certifications you can look into include:

• Certified Analytics Professional (CAP) 
• Open Certified Data Scientist (Open CDS)
• Microsoft Certified Azure Data Scientist Associate
• SAS Certified Advanced Analytics Professional
• IBM Data Science Professional Certificate
• Google Data Machine Learning
• SAS Certified Data Scientist

Get an Internship

Statistics show that 56% of all interns in the U.S. accept job offers from their internship company and students with internship experience are 35% more likely to receive at least one job offer. Therefore, internships serve as a crucial step to becoming a data scientist. They offer invaluable opportunities to gain practical experience and hands-on exposure to real-world data science projects. 

By working alongside experienced professionals in data science or related fields, interns have the chance to observe best practices, learn from mentors and collaborate on meaningful projects that contribute to organizational goals. Additionally, internships offer networking opportunities, allowing individuals to forge connections within the industry and potentially pave the way for future employment opportunities.

Consider Getting a Master's Degree in Data Science

Pursuing a master's degree in Data Science can be a strategic pathway for individuals looking to advance their careers and deepen their expertise in this rapidly growing field. Master's programs in data science offer a specialized curriculum that covers advanced topics such as data mining, machine learning, predictive analytics and artificial intelligence. 

Unlike undergraduate programs, which provide a broad overview of various disciplines, master's programs enable students to focus more closely on data science, enhancing their skills and understanding in a specialized area. This depth of knowledge can not only improve one's proficiency but also make them subject matter experts in their field, opening doors to advanced career opportunities and leadership roles.

Moreover, pursuing a master's degree is a necessity to stay competitive in the industry and potentially pave the way for future employment opportunities. Recent data shows that around 34% of data scientists hold a master's degree and an additional 13% possess a doctorate, highlighting the significance of advanced education in data science.

Start your Data Science Career

Networking is key to breaking into the data science industry. So, attend industry events, seminars and conferences to connect with peers, mentors and experts. The Midwest Big Data Innovation Hub, of which the University of North Dakota is a founding member and whose collaboration continues , is an excellent initiative that provides fantastic networking opportunities through collaborative projects and events. Building a strong professional network can give you valuable insights, mentorship and even job opportunities.

Once you've developed your data science skills and begun putting together your portfolio, it's time to start looking for jobs. To increase your chances of landing a data science job, tailor your resume and cover letter to highlight your relevant experiences, projects and technical skills.

Remember that persistence and continuous learning are vital traits to have in the competitive field of data science. 

Job Outlook and Salary

As organizations increasingly rely on data to inform important decisions and improve business processes, the volume of available data and its potential applications are expected to skyrocket. As a result, the need for data scientists to extract, analyze and interpret this vast amount of information continues to increase. 

The projected growth rate for this occupation from 2022 to 2032 is 35%, far surpassing the average growth rate for all occupations. Over the next decade, this growth is expected to result in around 17,700 job openings annually.

Data science is also a highly lucrative career path, with a median annual wage of $103,500 . While entry-level salaries may start at around $58,510, top earners in the field can command more than $174,790 annually. This reflects the high value placed on skilled data scientists in today's data-driven economy.

Conclusion 

While there may be various paths to becoming a data scientist, the approach emphasized in this article, which prioritizes formal education and continuous learning, offers a structured and reliable method to ensure students acquire the essential skills and knowledge required for success. 

UND recognizes the significance of this approach, offering data science degree programs at both the bachelor's and master's levels and providing support to help students build valuable connections in the industry. So, join us and unlock the power of data to shape the future.

Can you become a data scientist without a degree? ( Open this section)

Yes, becoming a data scientist without a degree is possible. Many successful professionals in the field have diverse educational backgrounds. However, this route doesn't always ensure success, so pursuing formal education and training can provide a more dependable route to develop essential skills and secure employment in this field.

How long does it take to become a data scientist? ( Open this section)

The timeline to become a data scientist varies. Typically, it involves several years of education and training. Bachelor's degrees usually take around four years and further training or a master's degree may require additional time.

Is it difficult to become a data scientist? ( Open this section)

Becoming a data scientist requires dedication and access to the right resources. It's challenging but achievable. Success in this field demands a combination of technical skills and continuous learning to keep pace with evolving trends and technologies.

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