professional summary in resume for data scientist

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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17 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 17 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.

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 10 fresh and  free Google Docs resume templates  that can make your  resume-building  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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Gut the guesswork in your job hunt. Upload your existing resume to check your score and make improvements. Build a resume with one of our eye-catching, recruiter-friendly templates.

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12 Data Scientist Resume Examples - Here's What Works In 2024

Data scientists are one of the hottest jobs of 2023. however, it’s also one of the most analytical, results-driven, and requires superb use of numbers. if you can show that on your resume, you’ll be on your way to a nice career as a data scientist. here are five data scientist resume templates to help you get an idea of what to put in your resume..

If career growth is one of your main qualifications for your next job, a career in data science is perfect for you. According to Towards Data Science , it’s the fastest-growing job on LinkedIn with an estimated over 11 million jobs by 2026. And it deserves to have such a bright future. You can apply for this job in several industries like e-commerce, IT, business, and much more. Because this field is so versatile, you can apply your skills somewhere that would greatly benefit others, not just a company. For example in healthcare, you can help visualize and manage data necessary for operation procedures. For a job like this, you need to be good with numbers and data. The ability to use statistics, analyze complex data, simplify it, and present it more easily for others are all necessary components of the job. You’ll need to display these skills, plus some experience with computer programs like Amazon Web Services to handle big data, in your resume. Today, we’ll be sharing with you the tips you need to make a data scientist resume that recruiters will look at.

Data Scientist Resume Templates

Jump to a template:

• Data Scientist
• Senior Data Scientist
• Entry Level Data Scientist
• Data Science Manager
• Data Science Vice President
• Junior Data Scientist
• Career Change into Data Science

Jump to a resource:

• Keywords for Data Scientist Resumes

Data Scientist Resume Tips

• Action Verbs to Use
• Bullet Points on Data Scientist Resumes
• Frequently Asked Questions
• Related Data & Analytics Resumes

Get advice on each section of your resume:

Template 1 of 12: Data Scientist Resume Example

A data scientist uses and processes raw data to discover interesting insights that help organizations make more informed decisions. They are part of the entire life cycle of data science projects. This means they work on collecting and storing data, as well as in data processing, developing data models, data analysis, and visualization. Cloud migration is now an in-demand skill for data scientists, due to the rapid adaptation of cloud services. Hence, it might be a good idea to include cloud migration skills on your resume.

We're just getting the template ready for you, just a second left.

Tips to help you write your Data Scientist resume in 2024

   include up-to-date data analysis or big data skill sets on your resume, like tinyml..

Data science is a fast-changing field, and hiring managers particularly at tech companies or startups love when candidates include recent technologies. One example is TinyML or other ML algorithms. Machine learning algorithms are perfect for processing large sets of data, especially when working with cloud-based systems with unlimited bandwidth. It might be worth including a project on your resume where you used ML or insights from an ML algorithm to improve the bottom line at your company (if you drove revenue or saved costs as a result of running a data science algorithm, hiring managers will be thrilled).

   Indicate your proficiency in data visualization tools like Tableau or Google Charts.

Mention projects in which you used your data visualization skills to present your insights. Data visualization plays a huge role in data science projects, so it’s important to demonstrate you have experience in this area.

Skills you can include on your Data Scientist resume

Template 2 of 12: data scientist resume example.

Because you are working with data that provide to you or you provide other departments data to use, you need to display successful collaboration with results in your resume. This sample does this by talking about what company goals were accomplished with other teams using metrics to highlight the achievements.

   Numbers and metrics relevant to data scientists

You can see examples of metrics to go with the companies’ achievements. For example, this person increased “customer traffic by 75%”, and generated “$1 million in wealth management sales”. Data science is always aligned with company KPIs, so list your achievements in a way that describes how you solved a company’s problem.

   Strong action verbs related to data scientists

When you read this sample, you’ll see words like “implemented”, “optimize”, and “reduced.” All these are action verbs that communicate the ability to do/succeed in a task. Include strong action verbs in your resume that communicates your ability to organize projects and collaborate with others.

Template 3 of 12: Senior Data Scientist Resume Example

Senior data scientists outline project requirements, delegate tasks to junior data scientists, monitor their performance and carry out upper-level responsibilities. Their purpose is to drive companies to success by using data analytics. Your potential employer might expect you to have extensive experience in data science, so it’s important to demonstrate seniority on your resume. You should prioritize relevant job experience and highlight your leadership background.

Tips to help you write your Senior Data Scientist resume in 2024

   indicate your proficiency in r, python, or other relevant programming languages by mentioning previous projects in which you used them..

Since most companies are generating a large amount of data, you need specific programming languages such as R or Python to process them. That’s why your potential employer might be looking for an experienced senior data scientist in these programming languages.

   Demonstrate experience in formulating and overseeing data-centered projects.

A senior data scientist is a leadership role. You will be supervising other junior data scientists to ensure they follow certain standards and processes, whether that involves cleaning or exploration. That’s why it is important to demonstrate on your resume that you have experience with developing and monitoring these types of projects.

Skills you can include on your Senior Data Scientist resume

Template 4 of 12: senior data scientist resume example.

If you’re trying to climb up to the top of the data scientist ladder, you need to show that you excelled in lower positions. Don’t forget to list what you did that earned you an upper-level role in your previous job. Recruiters love to see that you desire to grow. Talking about your transitions is key in this kind of resume.

   Shows growth in promotions

In the sample, you see that there was a promotion within a short amount of time at a company. If you had a promotion, emphasize it by separating the job titles and explaining what work you’ve done that contributed to you getting promoted.

   Numbers and metrics relevant to senior data scientists

Don’t just list promotional achievements without also providing the metrics. Recruiters want to see how you’ve been beneficial to the previous company, and numbers are a great way to show your achievements. That gives recruiters an idea of how you can help their company out.

Template 5 of 12: Entry Level Data Scientist Resume Example

As an entry level data scientist, you'll be dipping your toes into the world of analyzing and interpreting complex data sets to help businesses make informed decisions. While the demand for data scientists has been booming in recent years, competition for entry-level roles can be fierce. To stand out, your resume should showcase your technical skills and demonstrate your ability to turn raw data into valuable insights for the company. Think about highlighting projects where you've used relevant programming languages, machine learning techniques, and data visualization tools. In addition to showcasing your technical expertise, don't forget to highlight any internships or relevant work experience you have related to data analysis. Companies are not just looking for technical wizards; they are also seeking individuals who can work well with others, translate complex findings into understandable insights, and ultimately drive business growth. Make sure to include any instances where you've collaborated with cross-functional teams or presented data-driven findings to non-technical stakeholders.

Tips to help you write your Entry Level Data Scientist resume in 2024

   show off your technical skills.

As an entry level data scientist, you should emphasize your programming abilities and proficiency in languages like Python, R, and SQL. Additionally, mention any experience working with data analysis tools, such as Tableau, to demonstrate your ability to visualize and communicate results effectively.

   Highlight your problem-solving capabilities

Data scientists need to be adept at solving complex problems and uncovering insights from raw data. Use your resume to share examples of how you've approached and solved data-related challenges, emphasizing your analytical mindset, creativity, and critical thinking skills.

Skills you can include on your Entry Level Data Scientist resume

Template 6 of 12: entry level data scientist resume example.

Right out of college, you may not have much experience in the field. To supplement that, use your experience in clubs and activities, class projects, and useful coursework to help highlight your knowledge on the subject. Internship experience is essential, as well; any numeric results or accomplishments should be acknowledged. This sample does so by listing the percentages of costs, labor, and hours reduced thanks to their work.

   Strong data scientist technical skills

Not only are key skills listed in the skills section (things like MATLAB or SQL), you can also see this sample mention the use of some of these skills throughout their experience. You should also include skills that are relevant to data science jobs that you have - review the job description that you're applying to for skills the job is looking for.

   University projects relevant to data scientists

Class projects are good examples of how a recent grad has applied critical job skills. In the descriptions, it also lists awards won. This shows that the projects they worked on were successful in applying what they learned to get results.

Template 7 of 12: Data Science Manager Resume Example

A data science manager has an administrative and technical role. They are responsible for guiding and overseeing the data science team. Hence, they will determine project outlines, deadlines, and priorities, and ensure team members follow specifications. As a data science manager, you should ideally have a master’s degree in data science or equivalent experience. You can take your resume to another level by demonstrating your impact on previous projects’ results. This way, you are showcasing your tangible value.

Tips to help you write your Data Science Manager resume in 2024

   include your data science certifications on your resume..

Your data science manager resume should highlight your academic value and expertise, and certification is a great way to demonstrate that. These are third-party validated credentials that exhibit your skills and years of experience.

   Highlight your project management skills through relevant work experience.

Data science managers should have project management skills to successfully drive success to the data science team. Recruiters are looking for past evidence of assigning tasks, prioritizing deliverables, providing feedback, conducting research, and ensuring team members’ performance. To highlight this, include action verbs like "Led" or "Managed".

Skills you can include on your Data Science Manager resume

Template 8 of 12: data science manager resume example.

To be a successful manager in any role, you need to have the experience of a manager. A focus on team management and leading a team to great results are examples you should list on your resume. Showing recruiters that you can lead a team or data science project that brings high-yield results is what will set your resume apart from other applicants. Data science is all about using data to drive decision-making and top-level KPIs, so make sure you add accomplishments to your resume that highlight how your work has affected your company’s bottom line.

   Emphasis on managerial skills

You can see in the experience section of this sample how they led a few projects. They discuss what was done, who they worked with, and how big a team they had. Follow a similar layout in your resume so recruiters can see that you can lead data science teams.

   Tailored to the data science industry

One way that you can get your resume past the filtering system, or ATS, is to use specific keywords that are found throughout the job description. In this sample, you see keywords like “training and peer-mentoring”, “data systems”, and “regression analysis.”

Template 9 of 12: Data Science Vice President Resume Example

A Data Science Vice President sits at the intersection of data analytics, business strategy, and leadership. In recent years, your role has evolved from pure data analysis to one where you're expected to guide an entire organization's data strategy. As companies increasingly rely on data-driven decision-making, you're not just crunching numbers but explaining their implications to non-technical executives. When crafting a resume for this role, remember companies are looking for a strategic thinker who can leverage data to drive business growth, not just a seasoned analyst. As the field becomes more competitive, hiring managers are expecting more than just top-notch technical skills. They want to see a track record of transforming raw data into actionable insights that drive business results. They're also looking for leaders who can build and guide high-performing data science teams. So, make sure your resume reflects these demands and trends.

Tips to help you write your Data Science Vice President resume in 2024

   highlight strategic leadership.

As a Data Science Vice President, you're expected to be a strategic leader. Highlight instances where you've used data to inform business strategy. Show how you've influenced decision-making at the executive level by translating complex data into digestible insights.

   Focus on Team Building and Management

This role isn't just about your expertise with data, but also your ability to lead a team. Detail your experience in building, leading, and mentoring data science teams. If you've overseen sizeable teams or managed across different locations, ensure that it shines on your resume.

Skills you can include on your Data Science Vice President resume

Template 10 of 12: data science vice president resume example.

Like any VP role, the position of vice president of data science needs strong managerial skills. Not only will you need to manage a team, but that team will also have to consist of managers. Your goal is to implement and execute company-wide goals that greatly benefit the company. This sample lists out the processes done while managing managers lower on the corporate ladder, to bring in an increase of profit or a decrease in costs (or increase in productivity).

In this sample, the positions listed are all higher than the ones listed below. That shows recruiters that you have the ambition to climb to the top. Additionally, with each upper management role, you see growth in the people they work with; they started with “hired 8 new candidates” and are now “worked closely with a cross-functional team.” Show your incline in managerial responsibilities in your resume.

   Focused on the vice president of data science role

In the upper management positions of this sample, you see how it talks about working with other department teams to deliver results that are often well over 40%. Positive metrics like this help show your abilities as a capable vice president.

Template 11 of 12: Junior Data Scientist Resume Example

Junior data scientists are just data scientists that have under five years of industry experience, or have recently made a career change into the field. The title is sometimes used interchangeably with the regular 'data scientist', so you can use this template whether or not you're a junior data scientist or have some experience in the field.

Tips to help you write your Junior Data Scientist resume in 2024

   numbers and metrics relevant to data scientists, and good use of skills relevant to data scientists..

You can see examples of metrics to go with the companies’ achievements. Plus, all the skills mentioned are very relevant to the data science and engineering field.

   Good use of space

The two-column in this data scientist resume template prioritizes the work experience sections, while maximizing the content into the resume. The resume does not look overcrowded and uses reasonable margins. Not all two column templates are ATS-compatible, but this one is when it is saved as PDF and passed through a resume screener.

Skills you can include on your Junior Data Scientist resume

Template 12 of 12: career change into data science resume example.

If you're trying to break into data science, but don't have formal data science experience yet, use a template like this one.

Tips to help you write your Career Change into Data Science resume in 2024

   stress transferrable skills from your previous experiences.

Even if you didn't do data science work in your previous professional roles, you have technical experience as well as leadership, teamwork and analytical skill sets.

   Use keywords and skills from the new industry on your career change resume

To get past the applicant tracking systems and resume screeners, it's important that you use the right keywords for your target job, which in this case is a data science position. Even though you might have sales or product marketing experience, use keywords that are specific to data science only - including things like SQL/database experience, ML/AI experience, and other data preparation tools and techniques.

Skills you can include on your Career Change into Data Science resume

We reached out to hiring managers and recruiters at top companies like Google, Amazon, and Microsoft to gather their best tips for creating a standout data scientist resume. Here's what they shared:

   Highlight your technical skills

Make sure to showcase your proficiency in the key technical skills required for data science roles, such as:

• Programming languages (Python, R, SQL)
• Machine learning frameworks (TensorFlow, PyTorch, scikit-learn)
• Data visualization tools (Tableau, PowerBI, Plotly)
• Big data technologies (Hadoop, Spark, Hive)

Don't just list the skills, but provide specific examples of how you've used them in projects or previous roles. Quantify your impact whenever possible, like 'Built machine learning models using Python and scikit-learn to improve customer churn prediction accuracy by 25%.'

   Showcase your projects and their impact

Hiring managers want to see evidence of your ability to apply data science techniques to real-world problems. Include 2-3 of your most impressive projects, highlighting:

• The business problem or question you were trying to solve
• The datasets and techniques you used (e.g., data cleaning, feature engineering, model selection)
• The results and impact of your work, quantified if possible (e.g., increased revenue, reduced costs, improved efficiency)

Even if the projects were part of coursework or personal learning, they can still effectively demonstrate your skills and problem-solving approach.

   Tailor your resume to the job description

Data science roles can vary significantly between companies and industries. Carefully review the job description for each position you apply to, and customize your resume accordingly.

Look for key skills, tools, and domain knowledge mentioned in the job requirements, and make sure to emphasize your relevant experience in those areas. For example, if the job heavily focuses on natural language processing (NLP), highlight any NLP projects or coursework you've completed.

   Provide context for your achievements

When describing your accomplishments, provide enough context to help the hiring manager understand the significance of your work. Instead of simply stating what you did, explain why it mattered to your team or organization.

• Developed a machine learning model to predict customer churn
• Developed a machine learning model to predict customer churn, enabling proactive retention efforts that reduced churn by 20% and saved the company $500K annually

By connecting your work to business outcomes, you demonstrate your ability to drive meaningful impact and think strategically.

   Show your communication and collaboration skills

Data scientists rarely work in isolation; they need to effectively communicate insights to stakeholders and collaborate with cross-functional teams. Highlight experiences that showcase these critical soft skills:

• Presenting findings to executive leadership
• Collaborating with engineers to deploy models in production
• Partnering with domain experts to define business problems and requirements

Worked closely with product and marketing teams to develop customer segmentation models, leading to personalized marketing campaigns that increased conversion rates by 30%.

By emphasizing your communication and collaboration abilities, you show that you can bridge the gap between technical and non-technical audiences.

   Demonstrate continuous learning and growth

The field of data science is constantly evolving, with new techniques and tools emerging regularly. Hiring managers want candidates who are committed to ongoing learning and staying up-to-date with industry trends.

Highlight any relevant coursework, certifications, or independent learning you've undertaken to expand your data science skills. This could include:

• Online courses (e.g., Coursera, edX, Udacity)
• Participation in data science competitions (e.g., Kaggle)
• Attendance at conferences or workshops
• Contributions to open-source projects

By showcasing your continuous learning efforts, you demonstrate your passion for the field and your ability to adapt to new challenges and technologies.

Data science is a broad job category. You could have a focus on designing machine learning algorithms/predictive analytics, or data visualization, or mathematics and statistics. You may even have more of a focus on the business side of things. No matter which area of data science you’re in, follow these tips to help you tailor the perfect resume.

   Think it all through first

Before you start filling out your resume, have a brainstorming session. What programs, teamwork-based, or other hard skills do you have that are relevant? What are some of the achievements you’ve had on the job? Did you do (and succeed) any data science projects? Have an idea of all of that first. Then, write it out in your experience. The key is to ensure you’re including quite a few metrics. A role that involves a lot of data requires someone who is good at handling big numbers and knows how to effectively use the info. If that data involves cooperation from another department, include that as well.

   Edit it so the resume is fitting for the job description

When you finish writing it, reread the job description. How well do you think you did in matching your resume’s keywords with the job opening’s keywords? Have you left out the filler information? (You should; only make space for what’s necessary, especially when you have lots of experience.)

  Include personal projects

For those of you who are transitioning from a different --but possibly somewhat relevant-- field, or are fresh out of school, projects are your friend. Just be certain to briefly describe what the project was for, what you accomplished, and provide metrics. Let’s say that you want to enter the finance field; an example project you can complete is a credit card fraud detector. You’ll use Python to track transaction history and spending habits, and use regression analysis to accurately track the two. You can also include links to your Github profile too, especially if you have a project that’s particularly relevant.

   Talk about collaborations with teams

For those of you who are veterans in the field, focus on your work done with other departments. Data science is all about working with other teams to drive business decisions, and teamwork is a skill that recruiters look for. What collaborative projects have you done that exemplifies this? Are/were you in charge of leading a team that brought in lots of revenue or extra work time? Have you been in charge of a major development project? Detail this information in your experience.

Writing Your Data Scientist Resume: Section By Section

  header, 1. put your name front and center.

Your name should be the most prominent element in your header, typically styled in a larger font than the rest of your contact details. This makes it easy for hiring managers to remember who you are.

Here's an example of how to format your name:

Avoid nicknames or unprofessional email handles:

• Johnny 'The Data Wizard' Smith
• [email protected]

2. Include essential contact details

Under your name, provide your key contact information:

• Phone number
• Professional email address
• Location (City, State)
• LinkedIn URL

Example of how to format this:

[email protected] | 555-123-4567 | Seattle, WA | linkedin.com/in/johnsmith

Avoid providing unnecessary personal details like your full mailing address or multiple phone numbers, which can clutter your header.

3. Optionally include your top data science credential

If you have an impressive, industry-recognized data science certification or credential, consider featuring it after your name to immediately boost your credibility. For example:

John Smith, CFA [email protected] | 555-123-4567 | Seattle, WA | linkedin.com/in/johnsmith

However, avoid listing multiple credentials or irrelevant certifications that may distract from your core qualifications as a data scientist.

  Summary

A resume summary is an optional section that sits at the top of your resume, just below your name and contact information. While not required, it can be a valuable addition for data scientists, particularly those with extensive experience or looking to transition into the field. A well-crafted summary provides context and highlights your most relevant qualifications, setting the stage for the rest of your resume.

When writing your summary, focus on your key strengths, experience, and accomplishments that align with the data scientist role you're targeting. Avoid using an objective statement, as it tends to focus on your goals rather than what you can bring to the employer. Instead, think of your summary as a snapshot of your professional profile, showcasing why you're the ideal candidate for the position.

To learn how to write an effective resume summary for your Data Scientist resume, or figure out if you need one, please read Data Scientist Resume Summary Examples , or Data Scientist Resume Objective Examples .

1. Highlight your technical expertise

As a data scientist, your technical skills are crucial to your success in the role. Use your summary to showcase your proficiency in key areas such as:

• Programming languages (e.g., Python, R, SQL)
• Machine learning algorithms and frameworks
• Data visualization tools (e.g., Tableau, PowerBI)
• Big data technologies (e.g., Hadoop, Spark)

For example:

Data Scientist with 5+ years of experience leveraging Python, R, and SQL to build and deploy machine learning models. Proficient in data visualization using Tableau and PowerBI, with expertise in big data technologies like Hadoop and Spark.

2. Quantify your impact

Hiring managers love to see concrete examples of how you've driven results in your previous roles. Use metrics and data to quantify your impact, demonstrating the value you've brought to your past employers. For example:

• Experienced data scientist with a passion for solving complex problems
• Collaborated with cross-functional teams to develop and implement data-driven solutions

While these statements provide some insight into your experience, they don't give the hiring manager a clear sense of your impact. Instead, try something like:

• Developed machine learning models that increased customer retention by 15% and reduced churn by 20%
• Led a team of 5 data scientists to optimize supply chain processes, resulting in $2M in annual cost savings

3. Showcase your industry knowledge

Demonstrating your understanding of the industry you're targeting can help you stand out from other applicants. Use your summary to highlight your experience working with industry-specific datasets, tools, or challenges. For example:

Data Scientist with 7+ years of experience in the financial services industry. Expertise in developing predictive models for fraud detection, risk assessment, and customer segmentation. Proficient in using industry-specific tools like Bloomberg Terminal and FactSet.

By showcasing your industry knowledge, you demonstrate to the hiring manager that you understand the unique challenges and opportunities within their sector, making you a more compelling candidate.

  Experience

Your work experience section is a key part of your data scientist resume. After all, it's where you show that you have the skills and experience to excel in the role.

Here are some tips to make sure your work experience section is as strong as it can be:

1. Highlight your technical skills

As a data scientist, you likely have experience with a variety of programming languages, tools, and frameworks. Make sure to highlight the ones that are most relevant to the job you're applying for.

Here are some examples of how you might showcase your technical skills:

• Developed machine learning models using Python, scikit-learn, and TensorFlow to predict customer churn with 95% accuracy
• Analyzed large datasets using SQL and Tableau to identify opportunities for cost savings and process improvements
• Built and maintained data pipelines using Apache Spark and Hadoop to process and analyze terabytes of data

Not sure if your resume highlights your technical skills effectively? Try using Targeted Resume to see how well your resume matches up with the job description. It can help you identify any key skills or keywords you may be missing.

Whenever possible, use numbers and metrics to quantify the impact of your work. This helps hiring managers understand the value you brought to your previous roles.

Here are some examples of how you might quantify your impact:

• Increased revenue by 20% by developing a predictive model to identify high-value customers
• Reduced data processing time by 50% by implementing a new data pipeline architecture
• Improved model accuracy by 10% by feature engineering and hyperparameter tuning

Contrast this with examples that don't quantify impact:

• Developed predictive models to identify high-value customers
• Implemented a new data pipeline architecture
• Improved model accuracy through feature engineering and hyperparameter tuning

If you don't have access to specific metrics, you can still quantify your impact by using numbers. For example, you might say "Analyzed data from over 10,000 customers to identify trends and patterns."

3. Showcase your problem-solving skills

Data scientists are often tasked with solving complex problems using data. Use your work experience section to showcase examples of how you've used your problem-solving skills to make an impact.

Here are some examples:

• Identified and resolved data quality issues that were causing inaccurate reporting, resulting in a 15% increase in data accuracy
• Developed a machine learning model to predict equipment failures, reducing downtime by 20% and saving the company $500k annually
• Collaborated with cross-functional teams to identify opportunities for process improvements, resulting in a 25% reduction in cycle time

When describing your problem-solving skills, try to focus on the impact of your work. How did your solutions benefit the company or your team?

4. Highlight your leadership and collaboration skills

While technical skills are important for data scientists, leadership and collaboration skills are also highly valued. Use your work experience section to showcase examples of how you've led projects or collaborated with others.

• Led a team of 5 data scientists to develop a new customer segmentation model, resulting in a 15% increase in marketing campaign effectiveness
• Collaborated with cross-functional teams including marketing, product, and engineering to develop and launch a new product feature that increased user engagement by 20%
• Mentored junior data scientists on best practices for data analysis and modeling, resulting in a 25% improvement in team productivity

If you're applying for a senior-level data scientist role, highlighting your leadership and collaboration skills can help you stand out from other applicants. Consider using Score My Resume to get feedback on how well your resume showcases these skills.

  Education

Your education section shows hiring managers that you have the necessary training and knowledge for the data scientist role. It also helps them gauge your career level. Here are some tips to write an effective education section on your data scientist resume.

1. Put your education at the top if you're a recent grad

If you graduated within the last 1-3 years, place your education section above your work experience. This is because your degree is likely your strongest qualification for the job at this stage in your career.

Include the following details for each degree:

• Name of institution
• Degree earned
• Graduation year
• Relevant coursework, projects, or academic achievements

Education Master of Science in Data Science, ABC University, 2022 Relevant Coursework: Machine Learning, Data Mining, Big Data Analytics, Statistical Modeling Capstone Project: Developed a predictive model for customer churn using Python and TensorFlow

2. Emphasize advanced degrees and certifications

If you have a master's degree, PhD, or professional certifications in data science or a related field, make sure to highlight these in your education section. Advanced credentials demonstrate specialized expertise that can set you apart from other candidates.

Examples of data science certifications to include:

• Certified Analytics Professional (CAP)
• SAS Certified Data Scientist
• IBM Data Science Professional Certificate
• Microsoft Certified: Azure Data Scientist Associate

Education PhD in Computer Science, XYZ University, 2018 Dissertation: A Novel Approach to Sentiment Analysis Using Deep Learning Certifications SAS Certified Data Scientist, 2020 Microsoft Certified: Azure Data Scientist Associate, 2021

3. Keep it brief if you're a senior data scientist

If you have several years of work experience as a data scientist, your education section should be concise. Hiring managers will be more interested in your professional accomplishments than your academic background at this stage.

Here's what a bad example might look like for a senior data scientist:

• Bachelor of Science in Mathematics, DEF University, 2005-2009. Graduated summa cum laude. Relevant coursework: Calculus, Linear Algebra, Probability Theory, Mathematical Statistics. Senior thesis on applications of graph theory.

Instead, keep it short and sweet:

• BS Mathematics, DEF University

Action Verbs For Data Scientist Resumes

The field is all about quantifying aand using data. In your resume, you need to explain what you did with the data you have. In the samples, you’ll see examples of action verbs like “implemented”, “developed”, “coached”, and more. Action verbs like these show that you know how to apply the knowledge you have to your work.

For a full list of effective resume action verbs, visit Resume Action Verbs .

Action Verbs for Data Scientist Resumes

How to write a data scientist resume.

Here are step-by-step instructions on how to write an effective resume for a data scientist role. This guide can be used by both entry-level and experienced data scientists as well as data scientist managers.

Basic steps for writing a Data Scientist resume

1.1: place important information in your header.

Place your name at the top of the resume followed by your professional email address, city/country, and phone number. You could also include the job title of your desired role—e.g., Data Analyst—to tailor your resume to the job. It is a good idea to include links to your professional website and online profiles such as LinkedIn and GitHub.

1.2: Select sections that highlight your most relevant experience

A Data Scientist resume needs sections for experience and education. Unless you are a recent graduate, you should list your experience section first. If you have carried out projects that highlight your data analysis skills, you can include a projects section that briefly describes the projects alongside metrics that show what you accomplished.

Use bullet points to showcase your experience as a Data Scientist

2.1: use the [action verb] + [task] + [metric] format for your bulleted points.

A bulleted list of your achievements in the work experience section will make your resume easy for data science hiring managers to skim. Each bullet point should highlight a specific task or achievement from your previous role. Take a look at the bullet point example below: "Modelled user-engagement framework that reduced churn rate using predictive modeling and clustering that reduced churn rate by 40%." Notice how the bullet point uses an action verb that is relevant to data analysis, "Modelled". We describe a task that was completed and use numbers and metrics to quantify the impact of our achievement.

2.2: Highlight collaborative work and initiative

For mid to senior Data Scientist roles, you will need to demonstrate you can take initiative and work with other departments. Talk about collaborating with other teams to drive business decisions. To land a Data Science Manager role, highlight how you led a team to great results in a data science project.

Get past resume screeners by including the right technical skills

3.1: use word or google docs resume template for your draft, then save it as pdf.

Start your resume with a simple template in Word or Google Docs format. This ensures your resume can be scanned easily by Applicant Tracking Systems, which are software used to screen resumes online. Convert your resume to PDF to ensure the formatting and layout appears correctly to a data science recruiter.

3.2: Use an online resume checker to make sure resume scanners can read your resume

If the ATS cannot read your resume, it will automatically discard your application before a Data Science recruiter gets to see it. Upload your resume for free to a resume scanner to ensure it can be read correctly and that the bullet points and sections are correctly constructed.

3.3: Include a technical skills section

Populate the skills section with hard skills and keywords that the resume filtering software will be looking for. Common skills for Data Scientists include Machine Learning, Python, SQL, R, Data Mining, Statistical Modeling, and Hadoop.

Finalizing your Data Scientist resume

4.1: include resume summary if you are changing careers or are a senior level hire.

While resume objectives are outdated and should never be used, a resume summary is an optional section at the top of your resume that can help direct a recruiter's attention to specific skills and achievements not listed in the rest of the resume. The summary can also include transferable skills for people shifting to Data Science from other careers.

4.2: Reread the job description as you edit your resume

When you finish writing your resume, reread the job description. This will give you a sense of how well your resume matches relevant keywords in the data scientist role. Check whether you have included examples of your impact, such as the amount of savings your company experienced because of the machine learning model that you implemented.

Skills For Data Scientist Resumes

Data science is a number-intensive, data-heavy field. It’s one thing to know how to read the data. You also need to convert that data in a way that makes a company’s overall processes smoother. Your list of skills should aid in showing that. Because you’d be using languages like Python or SQL, it’s important to state it beyond the skills section. Where possible, mention how you used these tools in your experience, whether that’s to process large data sets, discover insights or drive business decisions. If recruiters can see that you know how to use critical tools for the job on your resume, it’ll stand out more. Plus, your resume will get past resume screening tools/ATS since employers often filter resumes out by searching for skills they expect to see. Closely read the job description to find skills to include in your resume.

• Data Science
• Machine Learning
• Artificial Intelligence (AI)
• Deep Learning

Data Mining

• Python (Programming Language)
• Natural Language Processing (NLP)
• Apache Spark
• R (Programming Language)
• Predictive Analytics
• Predictive Modeling
• Software Development
• Statistical Modeling

Skills Word Cloud For Data Scientist Resumes

This word cloud highlights the important keywords that appear on Data Scientist job descriptions and resumes. The bigger the word, the more frequently it appears on job postings, and the more 'important' it is.

How to use these skills?

Resume bullet points from data scientist resumes.

You should use bullet points to describe your achievements in your Data Scientist resume. Here are sample bullet points to help you get started:

Conducted private equity due diligence in $400M portfolio. Performed strategic and analytical valuation of assets based on interviews with experts and created extensive models of the industries; persuaded client to move forward with acquisition

Analyzed data from 25000 monthly active users and used outputs to guide marketing and product strategies; increased average app engagement time by 2x, decrease drop off rate by 30%, and increased shares on social media by 3x over 6 months

Generated insights on customer churn and renewal rates from data tables with 100M rows in SQL

Liaised with marketing to drive email and social media advertising efforts, using predictive modeling and clustering, resulting in a 35% increase in revenue

Reduced signup drop-offs from 65% to 15%, increased user-engagement by 40%, and boosted content generation by 15%, through a combination of user interviews and A/B-testing-driven product flow optimization

For more sample bullet points and details on how to write effective bullet points, see our articles on resume bullet points , how to quantify your resume and resume accomplishments .

Frequently Asked Questions on Data Scientist Resumes

How can i improve my data scientist resume.

• Include a projects section that briefly describes the projects alongside metrics that show what you accomplished. Here, list projects that demonstrate the use of statistical methods, data visualization techniques and predictive models.
• Include the job title for the desired role—Data Scientist—on the resume header below your name. This makes your resume easier for screening software to categorize.
• Include links to your professional website and online profiles such as LinkedIn and GitHub.
• Include a summary section if you are a senior-level hire or are changing careers to direct the recruiter’s attention to transferable skills and exceptional achievements.

How does a data scientist’s resume differ from that of other data analytics roles?

What skills should you put on a data scientist resume, what are strong examples of bullet points i can include in my data scientist work experience.

Modelled a user-engagement framework that reduced churn rate using predictive modelling and clustering that reduced churn rate by 40%. Designed and implemented securities forecasting models, improving stock market forecast accuracy by 15%.

Other Data & Analytics Resumes

Director of Analytics

Solutions Architect

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Data Scientist Resume Guide

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• GIS Resume Guide
• Data Scientist Resume Example
• Senior Data Scientist Resume Example
• Entry Level Data Scientist Resume Example
• Data Science Manager Resume Example
• Data Science Vice President Resume Example
• Junior Data Scientist Resume Example
• Career Change into Data Science Resume Example
• Tips for Data Scientist Resumes
• Skills and Keywords to Add
• Sample Bullet Points from Top Resumes
• All Resume Examples
• Data Scientist CV Examples
• Data Scientist Cover Letter
• Data Scientist Interview Guide
• Explore Alternative and Similar Careers

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• • Turned data into actionable insights, providing C-suite stakeholders with insightful recommendations to streamline business operations and improve customer experience.
• • Generated statistical reports and visualizations, providing key insights for more than 20 marketing campaigns and initiatives, including A/B testing, customer retention, brand awareness, and global expansion.
• • Worked with senior leadership to develop and implement digital marketing strategy, identifying and implementing new tactics to improve campaign performance by 50%, resulting in 250% increase in revenue from search marketing campaigns.
• • Designed and implemented a machine learning system that predicts hardware malfunction with more than 80% accuracy.
• • Created global and personalized real time reports system for executives stakeholders and processes in SAS, Tableau, and proprietary systems.
• • Worked closely with a team of data engineers and BI analysts to improve the efficiency customer recommendation analytics engine by 33%.
• • Collected technical requirements for $500K+ customer accounts, defining data rules for and KPIs for performance metrics.
• • Perform HR data collection and a variety of statistical analyses using Microsoft Excel, SAS, Tableau and Python.
• • Assisted senior data science team in building innovative machine learning models and segmentations for personalization initiatives to drive margin, revenue, and conversion.

14 Data Scientist Resume Examples & Guide for 2024

Your data scientist resume needs to convey your expertise in data analysis and interpretation. Make sure to highlight your proficiency in programming languages such as Python or R. It's crucial that your experience with machine learning algorithms and data visualization tools like Tableau or PowerBI is evident. Your resume should reflect your ability to turn complex data into actionable insights.

All resume examples in this guide

Data Science Intern

Entry-Level Data Scientist

Senior Data Scientist

Machine Learning

Python Data Scientist

Associate Data Scientist

Data Science Manager

NLP Data Scientist

Metadata Scientist

Educational Data Scientist

Data Science Director

Data Science Consultant

Data Analytics Scientist

Senior Data Scientist | CAP | DASCA resume example

Resume Guide

Data Scientist Resume Example

Resume Format

Resume Experience Section

Hard & Soft Skills

Data Science Certifications

Resume Summary/Objective

Other Resume Sections

Key Takeaways

By Experience

Data science is a complex industry, and continues to evolve in today’s technological landscape.

Machine learning and ChatGPT may be booming right now, but it can be challenging to stay on top of these rapidly changing technologies.

Your data scientist resume needs to demonstrate your technical skills as well as your ability to communicate with others. Show the distinct value of each of your projects while avoiding redundancy.

Don’t worry, our guide will show you how to write an incredible data scientist resume that highlights your expertise in Python and SAS without overshadowing your interpersonal skills.

This guide will teach you:

• How to use our data scientist resume templates to make a good impression and attract recruiters’ attention.
• How to format your experience section so that hiring managers can see how you’ll impact success at their company
• How to showcase your skills in a way that shows you’re on top of industry trends and are the right candidate for your target job
• What recruiters look for and how to write a strong data scientist resume summary that gets callbacks

Looking for related resumes?

• Data Engineer Resumes ;
• Entry Level Data Analyst Resumes ;
• Tech Resumes ;
• SQL Developer Resumes ;
• Tableau Developer Resumes .

Data scientist resume example

How to format a data scientist resume

There are a few different resume formats to consider for your data scientist resume, but your best bet is to go with a reverse-chronological resume .

The focus of this format is on your work history listed in reverse-chronological order, just as the name suggests.

This is the best choice for senior data scientists who have been in the industry for 10 years or more. It brings attention to your career growth and shows your commitment to your work.

If you’re changing careers or just starting out as an entry-level candidate, a better choice is a functional resume format. This brings attention to your skills and away from your lack of experience.

When choosing a file format for your data scientist resume, always go with PDF. It provides extra security and ensures there won’t be any unwanted formatting changes.

Only use a DOC or another file format if the job application instructions explicitly say so.

As far as data scientist resume length, limit yours to one page. Recruiters don’t want to spend time reading through lengthy resumes, so stick to what’s relevant.

If hiring managers want to see more of your work, your GitHub link is there to guide them.

(CTA to ats checker - Enhancv to do)

The top sections on a data scientist resume:

What recruiters want to see on your data scientist resume:, how to create an impactful data scientist experience resume section.

The experience section is the core of your data scientist resume. It’s where you’ll let all your hard work shine.

To make the most impact possible, follow these key rules:

• Include only major and relevant positions - the 2-month stint behind the counter at your grandfather’s banana stand interests no one. But that job as a data engineer working on sales data for a national fruit reseller is something the recruiter needs to see!
• Make it reverse-chronological - it’s the resume standard, and it saves mental energy for the recruiter. List your most recent positions first.
• Focus on impact rather than responsibilities - data mining, statistical analysis, and data visualization will be on almost every data scientist’s resume. Instead, explain the impact you had rather than just listing job duties.

Let’s take a look at a data scientist resume experience section to see how to avoid a common mistake.

• • Created and presented models for loan success factors.
• • Did database manipulation of the Financial Aid Database.
• • Coordinated a team of data scientists.

What doesn’t work in this example:

• No quantitative metrics or measurable results
• Uses broad verbs like “did” and “coordinated” that don’t speak to success
• Leaves out industry-specific knowledge or skills

Let’s look at that example again with a few changes.

• • Designed and implemented models for loan success factors, achieving a 20% improvement of approval decision time.
• • Spearheaded complete database restructuring of the Financial Aid Database used across 16 different countries.
• • Coordinated a team of 20 data scientists working on 6 different projects for insurance, finance, marketing, and security departments.

What works in this example:

• Shows evidence of specific results by “achieving a 20% improvement of approval decision time”
• Shows project management skills by mention “team of 20 data scientists working on 6 different projects”
• Shows industry-specific “data restructuring” skills and reach of “16 different countries”

This version is a big improvement. It quantifies impact with measurable results and industry-specific skills.

Always focus on relevant achievements instead of general responsibilities and tailor every section of your resume to fit your target job.

How to quantify impact on your data scientist resume

Companies hire data scientists to provide solutions and maximize success. If you want hiring managers to give you a chance, you need to quantify impact on your resume.

Recruiters will be looking through a stack of resumes that all list “data visualization” and “algorithm development” as skills. It’s not enough just to list it. You need to prove it.

Provide evidence to support your claims by sharing specific achievements with measurable success. Use real data and numbers to quantify impact in every section of your resume.

Quantitative data that can strengthen your data scientist resume include:

• Increased sales revenue
• Reduced redundancy or errors
• Rate of engagement or number of users
• Improved algorithm accuracy
• Profit margin
• Time saved for the company
• ROI for projects

Use these metrics throughout your resume to show potential employers exactly how you’ve achieved succes in previous roles.

Writing an entry-level data science resume

Just because you’re a recent grad looking for your first job in data science, don’t start thinking “I’m done, I don’t have any experience yet!”.

You’re mistaken if you think you don’t have any experience. Consider including

• Course projects that involved data science work - surely you’ve practiced your skills on a few practical exercises you can list here. Just make sure you feature the new and exciting projects - no one wants to see the same tired Titanic Survivor project!
• Internships - no matter if it’s your uncle’s company or a university help gig, you probably learned a lot, including keeping up with deadlines, working well with others, and communicating data results to different audiences. Practical skills matter, even if they’re soft skills.
• Volunteer work or side projects - if you don’t have practical experience, create some. There are tons of local SaaS startups that would benefit from logistic regression analysis to uncover their user activation points - help them out and use that as a practical example in your resume.

As you can see, there is a lot going on beyond traditional 9-to-5 steady job experience. And all of these will look great on your data scientist resume!

Looking to build your own entry-level job resume? Follow the steps in our guide on How To Write Your First Job Resume .

How to list your hard skills and soft skills on your resume

A data scientist needs a unique set of skills that lets you explore, transform, visualize and model datasets, and also communicate constantly with diverse stakeholder groups.

Make a good impression by showing that you have the right combination of hard skills and soft skills to accomplish this.

In “ Top 10 Big Data Skills to Get Big Data Jobs ” Amit Verma presents a comprehensive list of languages and systems data scientists should be able to work with, including

Top data scientist technical skills

Make sure you include only things that you know well enough to start working with tomorrow. There’s no point in inflating expectations and then missing the mark.

What about soft skills?

Just knowing the technology won’t cut it, you need soft skills too. We list some great ones below, and you can check out KDnuggets list of important soft skills .

Data scientist soft skill examples

The world of data is complex. Demonstrate that you can navigate through it, but also help others orient themselves in it. Make sure you cover this, especially for more senior positions where presenting to managers is everyday work.

How to list your certifications and education on your resume

You’ve come a long way to becoming a data scientist. You’ve put in a ton of hours reading O’Reilly textbooks, debugging Python scripts, and creating visualizations in Tableau.

Make all your hard work show on your resume. For a stellar education section, add info on

• Your university and major
• Your GPA and final marks
• Key courses relevant to the position you’re applying for
• Any awards you received or societies you were part of

Since data science is a relatively new field, it’s common for professionals to come into it from different fields. If this is the case for you, you can shorten your education section and include additional courses and certifications you’ve earned.

Top 20 data scientist certifications you can take:

Make sure you follow a few rules when presenting certifications on your resume:

• Make them stand out - don’t bury your certifications in another resume section, give them their own
• Add any capstone projects you worked on - certifications usually make you show what you learned in practice, prove that you can do what you say
• Show them your drive - if you completed the certification course quickly, mention it on your resume. It shows dedication and motivation to learn.

How to write your data scientist resume summary or objective

You may have heard the terms summary and objective used interchangeably when talking about resumes.

To get specific, a summary typically captures your industry experience and a few career highlights in 2-3 sentences. An objective talks about what you want to achieve in the future.

These days they are usually combined into one statement and referred to by either name.

A good formula for your data scientists resume summary is to write 2-3 sentences that cover the following points:

• Your title and role in the industry
• A top career highlight
• A shared goal of you and your potential employer

Let’s look at an example that uses this template.

• Specific number of years of experience and industry focus
• Shares a measurable result that achieved “94% accuracy”
• States shared goal to “increase engagement with Python modules” with target employer

Additional sections for a data scientist resume

Depending on your experience and career path, there may be additional sections you want to include on your resume.

• Projects - including a section for projects can be key in increasing the value of a data scientist resume. Potential employers want to know how you’ve used your practical skills, and a successful project is a great way to show that.
• Awards - important industry achievements or competitive awards can be a great way to show your value. Include any relevant awards you’ve earned in the field.
• Volunteer work or hobbies - not all practical experience has to come from a job. There are plenty of ways you can develop relevant skills through volunteering or hobbies. Include any experiences that speak to your industry knowledge.
• Publications - a good data scientist is a clear communicator as well as a numbers person. Publications will highlight your ability to clearly communicate complex ideas.

Remember that publications aren’t just research papers published in peer-reviewed journals. This section can also include links to blog posts you’ve written to show that you can speak in more than just an academic tone.

Key takeaways for writing a competitive data scientist resume

To sum it all up, a great data scientist resume should tick these boxes:

• Make a good impression and show your body of work with links to your GitHub portfolio and LinkedIn profile
• Demonstrate practical knowledge and quantify impact with measurable results so hiring managers will know that you can achieve success
• Show how your skills align with the requirements in the job description by tailoring every section of your data scientist resume to your target job
• Include additional sections that show that you stay on top of industry trends and are the right candidate for the job

Now you’re ready to create your amazing data scientist resume and land an interview for your next job!

Data Scientist resume examples

Explore additional data scientist resume samples and guides and see what works for your level of experience or role.

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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

Every journey begins with that all-important first step. Make sure that your career journey is on the right path by relying on the proven success of resume experts at TopResume. Contact us today to find out how you can benefit from a professionally crafted resume!

Introduction to TopResume: Professional resume writers

TopResume’s resume writers are more than just experts in the art of crafting compelling narratives for job seekers. They’re also some of the industry’s top experts in career planning, job search strategies, and coaching. Each member of our team is dedicated to helping you to succeed in reaching your career objectives and understands how important it is for your resume to capture the attention and interest of every hiring manager who sees it.

The TopResume resume creation process is second to none. The experienced writer tasked with crafting your resume narrative will work closely with you at every step of the way, to ensure that the final product conveys the message you need to be successful in your job search. When that process is complete, you will have a tailored resume that powerfully showcases your skills, relevant experience, achievements, and value as an employee.

Why you should make use of our resume writing services to land your next job as a Data Scientist

While almost anyone can create a resume, crafting a truly compelling narrative can be a challenge for many people. The TopResume team takes pride in its ability to provide every client with the resume needed to break through any hiring obstacles. Your assigned resume writer will be your partner in a collaborative effort to create a great resume that effectively tells your story to potential employers. By partnering with TopResume, you can have confidence that your resume will get the attention you deserve!

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.

Who are the TopResume writers?

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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

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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6 Data Scientist Resume Examples to Land You a Role in 2023

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.

Generate Your Resume Summary with AI

Speed up your resume creation process with the ai resume builder . generate tailored resume summaries in seconds., how to impress with your data scientist work experience:, best practices for your work experience section:.

• 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:.

• Information technology
• Computer science
• 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.

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.

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.

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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LinkedIn Data Scientist Resume Examples

Published September 19, 2023 9 min read

This article provides a comprehensive guide on crafting an effective LinkedIn profile for data scientists. It delves into the essentials of showcasing your academic qualifications, professional experience, and technical skills in a compelling manner. The piece emphasizes the importance of using industry-specific keywords to increase visibility, articulating your achievements quantitively, and demonstrating your proficiency in relevant tools and programming languages. It also discusses how to effectively exhibit your projects and research work, and how to make use of recommendations and endorsements to bolster your profile. This article is designed to aid data science professionals in curating a powerful LinkedIn profile that stands out to potential employers.

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LinkedIn Data Scientist Resume Example

Israel Kuno, Data Scientist

[email protected]

(577) 501-8348

Louisville, KY

Professional Summary

Data Scientist with one year of experience in leveraging data-driven models to solve complex business problems and drive strategic decision making. Possesses strong expertise in statistical modeling, data mining, and machine learning techniques. Proven ability to communicate complex analyses to non-technical audiences. Skilled in Python, R, SQL, and visualization tools. Adept at working in cross-functional teams and managing multiple projects simultaneously. Demonstrated ability to deliver valuable insights via data analytics and advanced data-driven methods.

Work Experience

Data Scientist at Humana Inc., KY

Apr 2023 - Present

• Developed a predictive model that improved the accuracy of patient risk assessment by 35%, significantly enhancing patient care and reducing healthcare costs.
• Implemented advanced data analytics techniques that led to a 20% increase in operational efficiency, saving the company approximately $2 million annually.
• Led a team that designed and launched a machine learning algorithm which improved fraudulent claims detection by 40%, preventing losses of around $5 million in the first year of implementation.

Junior Data Scientist at General Electric Company, KY

Sep 2022 - Mar 2023

• Led a team to develop an analytics model that improved the efficiency of GE's supply chain operations by 25%, resulting in annual cost savings of $1.2 million.
• Conducted detailed data analysis that identified $500,000 in potential revenue through the implementation of new business strategies.
• Designed a predictive model for machinery failure that reduced unplanned downtime by 15%, leading to an increase in productivity and saving the company $800,000 annually.

Master's Degree in Data Science at University of Louisville, KY

Aug 2017 - May 2022

Relevant Coursework: Machine Learning, Data Mining, Big Data Analytics, Data Visualization, Statistical Modeling, Predictive Analysis, Database Systems, Artificial Intelligence, Computer Programming, Cloud Computing, Data Structures and Algorithms.

• Python, R, SQL, Hadoop, Tableau, TensorFlow, Spark

Certificates

• Certified Data Scientist (CDS) from The Data Science Council of America
• IBM Data Science Professional Certificate

Tips for Writing a Better LinkedIn Data Scientist Resume

1. Highlight Relevant Skills: Start with listing your technical skills such as knowledge in programming languages (Python, R), machine learning algorithms, data visualization tools (Tableau, Power BI), statistical analysis, database querying languages (SQL), big data platforms (Hadoop, Spark).

2. Showcase Your Experience: Clearly explain your past experience in data science roles. What projects have you worked on? What were the results and impacts of these projects? Use quantifiable achievements to demonstrate your contributions.

3. Education and Certifications: List all relevant degrees and certifications related to data science. This could include a degree in computer science or statistics, or certifications from online platforms like Coursera or edX.

4. Use Keywords: Many recruiters use LinkedIn's search function to find potential candidates. Make sure you use keywords that are applicable to the job you're applying for throughout your profile.

5. Include a Strong Summary: Write a concise yet powerful summary that highlights your skills, experience, and career goals as a Data Scientist.

6. Detail Your Roles: For each position held, provide details about what you did in terms of projects and responsibilities.

7. Provide Evidence of Your Work: If possible, link to examples of your work such as GitHub repositories or blog posts where you've explained complex concepts or methods.

8. Recommendations and Endorsements: Request recommendations from colleagues or managers who can vouch for your skills and expertise.

9. Join Relevant Groups: Participating in relevant groups can show that you're engaged in the data science community.

10. Update Regularly: Keep your profile updated with any new skills learned or projects completed.

11. Proofread Your Profile: Make sure there are no spelling or grammar mistakes on your profile as it represents your professional image.

12. Be Active on LinkedIn: Post regularly about industry trends or share insightful articles to showcase your interest and understanding about the field.

Related : Data Scientist Resume Examples

Key Skills Hiring Managers Look for on LinkedIn Data Scientist Resumes

When you apply for a Data Scientist role at LinkedIn, it is crucial to incorporate keywords from the job description into your application. This is because LinkedIn, like many other companies, uses Applicant Tracking Systems (ATS) to manage their recruitment process. ATS are essentially software applications that can sort through thousands of resumes in a short period of time. They filter applications automatically based on given criteria such as keywords related to the skills, qualifications and experiences required for the job. By incorporating these keywords into your resume and cover letter, you increase your chances of passing this initial screening and having your application reviewed by a human recruiter. Without these keywords, even if you are highly qualified, the system might overlook your application. Therefore, understanding and leveraging this aspect can give you an edge in the highly competitive job market.

When applying for data scientist positions at LinkedIn, you may encounter a list of common skills and key terms.

Related : Data Scientist Skills: Definition and Examples

Common Action Verbs for LinkedIn Data Scientist Resumes

Crafting a compelling resume, especially for a data scientist position on LinkedIn, can be challenging. One of the main difficulties lies in finding varied action verbs to describe your experiences and skills. The use of repetitive or common verbs can make your resume blend into the crowd, reducing its effectiveness. Using diverse and powerful action verbs, however, can significantly enhance your resume by making it more engaging and attention-grabbing. These verbs not only highlight your abilities and accomplishments in a clear and concise manner but also paint a vivid picture of your professional journey, thereby helping potential employers understand your value better. Therefore, investing time to carefully select diverse action verbs is crucial for creating an effective LinkedIn Data Scientist Resume.

To provide you with a competitive advantage, we have put together a list of impactful action verbs that can enhance your resume and help secure your next interview:

Related : What does a Data Scientist do?

Editorial staff

Brenna Goyette

Brenna is a certified professional resume writer, career expert, and the content manager of the ResumeCat team. She has a background in corporate recruiting and human resources and has been writing resumes for over 10 years. Brenna has experience in recruiting for tech, finance, and marketing roles and has a passion for helping people find their dream jobs. She creates expert resources to help job seekers write the best resumes and cover letters, land the job, and succeed in the workplace.

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A data specialist shares the 2-page résumé that got him a $300,000 job at Google — and explains 3 details he got right on it.

• Ankit Virmani made a career switch from consulting to tech.
• After a full day of work at Deloitte, he would spend hours every night teaching himself how to code.
• The résumé that landed Virmani a job at Google is two pages long — a decision he defends today.

Ankit Virmani had spent five years in consulting when he began eyeing a shift to tech.

"I always thought in my heart that I wanted more technical depth. I wanted to build things rather than sell them too much," said Virmani, who first moved to the US from India to pursue a master's degree.

In the first half of 2020, he dove right in.

After wrapping up a day at his full-time job at Deloitte, Virmani would spend three to four hours practicing coding every night, and another two hours reading up about the industry. He also began spending time with people in the field, asking them about real-time scenarios and what challenges they face in their jobs.

"I didn't want answers from them. I wanted their thought process —how do they navigate through these complex challenges at scale," he told Business Insider.

It didn't pay off right away. He was rejected by Microsoft and Amazon at different stages of their application processes.

Six months after deciding to switch careers, he landed a role as a data and machine learning specialist at Google's Seattle office.

Related stories

Here's the résumé he used to apply for his job at Google, which pays more than $300,000 a year. BI has verified his employment and compensation.

Sacrificing the 'one-page only' résumé rule

Looking back on his résumé four years later, Virmani said he would make some formatting changes.

"This résumé is giving importance to everything equally, which is what I don't like," he said. "I would have a gradient of importance, like executive summary on top, achievements so far, and then I would go to professional experience, education, and technical skills."

But with more insight into what employers like Google appreciate, Virmani said he would keep several things the same — including the length of the document.

Sacrificing the "one-page only" rule to improve readability: Virmani broke the "one-page only" rule and prioritized having an uncluttered résumé. "It has very neatly structured sections and high-level themes," he said about using subheadings like "data architecture" and "cloud strategy." His manager at Google later told him that style helped them pick up on his responsibilities without having to decipher the lines below.

Highlighting team effort: Virmani said some people overly highlight individual contributions on their résumé: "It's never that way, at least in my experience — it's always teamwork." That's why he focused parts of his résumé on his teams' accomplishments. "In my experience, Google highly, highly appreciates honesty and humility. That's the culture of the company — we know that nothing great gets achieved by an individual," he said.

Saving some details for the interview: Virmani said he was careful not to over-explain his past projects so that he could build curiosity and have a good conversation during the interview: "If you put everything in the résumé, you'll run out of points to talk about in the interview."

Virmani is not alone in choosing to sacrifice "typical" résumé decisions. For Shola West, that came in the shape of breaking the "no résumé gap" idea.

West is part of a growing group of Gen Zs who are trying to destigmatize the résumé gap — a period of unemployment between jobs or between education and work.

West previously told BI she took a yearlong break at the start of her career to understand what she really wanted to pursue. She embraced her résumé gap and now works at an advertising agency and runs a career advice side hustle.

For Mariana Kobayashi, breaking from the résumé norms meant abandoning the written format altogether.

Kobayashi landed a role as an account executive at Google after she curated a video about why she should get the role.

She sent her video résumé, which took her 10 hours to create, to the hiring manager directly, Kobayashi previously told BI. A Google recruiter saw the video and reached out to her, and she eventually landed a role at the tech giant.

Do you work in finance or consulting, and have a story to share about your personal résumé journey? Email this reporter at [email protected] .

On February 28, Axel Springer, Business Insider's parent company, joined 31 other media groups and filed a $2.3 billion suit against Google in Dutch court, alleging losses suffered due to the company's advertising practices.

Watch: Lorraine Twohill, chief marketing officer at Google, says inclusive advertising is just good business

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