entry level data scientist resume summary

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Entry Level Data Scientist Resume Template

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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.

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Recruiter Insight: Why this resume works in 2022

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.

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.

Graduate Data Scientist Resume Sample

Data scientist resume sample, data analyst resume sample.

We spoke with hiring managers and recruiters at top companies like Google, Amazon, and Microsoft to gather their best tips for creating a strong entry-level data scientist resume. By following their expert advice, you can create a resume that will help you stand out from the competition and land your first job in this exciting field.

   Highlight your technical skills and tools

As an entry-level data scientist, it's important to showcase your technical skills and the tools you've used in your projects or coursework. This helps employers quickly see if you have the right skills for the job.

Include skills like:

• Programming languages: Python, R, SQL
• Machine learning libraries: scikit-learn, TensorFlow, Keras
• Data visualization tools: Tableau, Matplotlib, Plotly
• Big data tools: Hadoop, Spark, Hive

Avoid just listing the skills without context. Instead, show how you used them:

Used Python and scikit-learn to build and evaluate machine learning models, improving customer churn prediction accuracy by 20%.

   Show your impact with numbers and metrics

Employers want to see the impact you've made, even in entry-level roles or projects. Use numbers and metrics to quantify your achievements.

Compare this:

• Built a machine learning model to predict customer churn
• Developed a machine learning model using Python and XGBoost that predicted customer churn with 85% accuracy, enabling proactive retention efforts that reduced churn by 10%

The second example is much stronger because it includes specific numbers around model performance and business impact. Aim to include at least 2-3 measurable achievements for each role or project.

   Include relevant projects and coursework

As an entry-level candidate, you may not have much professional experience yet. That's okay - include relevant projects, coursework, or internships to show your skills and potential.

For example:

Capstone Project, Master's in Data Science, XYZ University • Collaborated with a team of 4 to predict loan default risk using machine learning, working with a dataset of 10,000+ loans • Built and optimized models using logistic regression, decision trees, and random forests, achieving an AUC of 0.89 • Presented findings and recommendations to a panel of professors and industry experts

This project description highlights teamwork, technical skills, and the ability to present findings - all important qualities for a data scientist.

   Tailor your resume to the job description

Customize your resume for each job you apply to by mirroring the language and skills from the job posting. This shows employers you're a strong fit.

For example, if a job description mentions:

• Experience with SQL and Tableau
• Strong communication skills

Make sure to include those skills on your resume, along with specific examples. You might write:

• Analyzed customer data using SQL queries and created interactive dashboards in Tableau to visualize key metrics • Presented findings to cross-functional teams and executives, communicating complex data insights in a clear and actionable way

   Highlight your unique background and perspective

Data science teams benefit from diverse perspectives and backgrounds. If you have unique experiences or skills, highlight them on your resume.

• Fluent in Spanish and English, enabling communication with a wider range of stakeholders
• Volunteer experience teaching coding to underrepresented youth, demonstrating passion for making tech more accessible

Avoid just listing unrelated hobbies or interests:

• Hobbies: Reading, hiking, cooking

Instead, focus on experiences and skills that showcase relevant traits like leadership, communication, or analytical thinking.

   Emphasize your communication and collaboration skills

Data scientists don't work in isolation - they collaborate with cross-functional teams and communicate findings to stakeholders. Highlight your communication and teamwork skills on your resume.

You might include:

• Presented data insights to executive team in weekly meetings, influencing decisions that led to a 15% increase in marketing ROI
• Collaborated with data engineers and business analysts to design and implement a new data pipeline, reducing processing time by 40%

Avoid just saying you have 'strong communication skills'. Instead, show how you've used those skills to drive results.

Writing Your Entry Level Data Scientist Resume: Section By Section

  summary.

When applying for entry-level positions, a resume summary is optional. Your resume already serves as a summary of your qualifications, so you don't necessarily need to include an additional summary at the top. However, a well-crafted summary can be beneficial in certain situations, such as when you're changing careers or have limited work experience. It allows you to provide context and highlight relevant skills that may not be immediately apparent from the rest of your resume.

Keep in mind that a resume objective, which focuses on your career goals rather than your qualifications, should be avoided. Instead, use the summary to showcase your strengths and explain how they align with the position you're seeking.

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

1. Tailor your summary to the entry-level position

When writing your summary for an entry-level position, it's crucial to tailor it to the specific job you're applying for. Research the company and the role to identify the key skills and qualifications they're looking for. Then, incorporate those keywords and phrases into your summary to demonstrate your fit for the position.

Here's an example of a tailored summary for an entry-level marketing position:

Recent marketing graduate with a passion for digital marketing and social media. Skilled in content creation, data analysis, and SEO. Seeking to leverage my knowledge and internship experience to contribute to ABC Company's marketing team and drive brand awareness.

Avoid using a generic summary that could apply to any entry-level position, like this:

Hardworking and motivated individual looking for an entry-level position to start my career. Fast learner and team player with strong communication skills.

2. Highlight relevant skills and experiences

As an entry-level candidate, you may not have extensive work experience. However, you can still showcase relevant skills and experiences in your summary. Consider including:

• Relevant coursework or academic projects
• Internships or volunteer work
• Transferable skills gained from part-time jobs or extracurricular activities

Recent business administration graduate with a focus on finance. Completed a capstone project analyzing the financial performance of a local startup, resulting in recommendations for cost savings and revenue growth. Proficient in financial modeling and data visualization.

Avoid focusing on soft skills or personal qualities without providing evidence:

• Team player with strong work ethic
• Detail-oriented and organized

  Experience

The work experience section is one of the most important parts of your resume as an aspiring data scientist. It's where you highlight your most relevant experiences, skills, and accomplishments to show hiring managers you have what it takes to excel in the role.

Follow these tips to write a compelling work experience section that will capture the attention of employers and help you land your first data science job.

1. Highlight data science projects and internships

As an entry-level data scientist, you may not have years of professional experience under your belt. That's okay! Instead, focus on highlighting relevant projects, internships, or coursework that demonstrate your data science skills.

• Developed machine learning models using Python and TensorFlow to predict customer churn, resulting in a 20% reduction in churn rate
• Analyzed large datasets using SQL and presented findings to stakeholders
• Used natural language processing techniques to classify tweets as positive, negative, or neutral
• Achieved 85% accuracy on test set

2. Use strong data science verbs

When describing your experiences, use strong, industry-specific verbs that highlight your data science skills and accomplishments. Avoid generic phrases like "responsible for" or "participated in."

Instead of this:

• Responsible for analyzing data and creating reports
• Analyzed large datasets using Python and Pandas to uncover insights and presented findings to stakeholders through data visualizations and reports

3. Quantify your impact with metrics

Whenever possible, use specific metrics and numbers to quantify your impact and achievements. This helps hiring managers understand the scope and significance of your work.

Developed a recommendation engine using collaborative filtering, resulting in a 30% increase in user engagement and $500K in additional revenue

If you don't have access to specific metrics, you can still provide context for your accomplishments:

• Built and maintained a database of over 100,000 customer records
• Created data visualizations that were used in weekly executive presentations

4. Tailor your experiences to the job

When applying for data science roles, it's important to tailor your work experience section to the specific job and company. Review the job description and identify the key skills and qualifications the employer is looking for.

For example, if a job description emphasizes experience with machine learning, make sure to highlight any projects or coursework where you applied machine learning techniques.

• Built and trained neural networks using TensorFlow to classify images of handwritten digits
• Experimented with different architectures and hyperparameters to improve model performance

  Education

Your education section is an essential part of your entry level data scientist resume. It shows hiring managers that you have the necessary background and training for the role. Use these tips to make sure your education section is effective and tailored to data science jobs.

1. Put education at the top for entry level roles

When you're applying for entry level data scientist positions, your education is often your strongest qualification. Put your education section at the top of your resume, above your work experience.

Include the following details for each degree:

• Name of school
• Degree earned
• Graduation year
• Relevant coursework, honors, or awards

Master of Science in Data Science, ABC University, 2023 Relevant Coursework: Machine Learning, Big Data Analytics, Statistical Modeling GPA: 3.8

2. Highlight data science coursework and projects

In addition to your degree, include relevant coursework, projects, or achievements from your data science program. This gives hiring managers more insight into your skills and experience.

• Completed capstone project using NLP to analyze customer reviews
• Relevant Coursework: Data Mining, Data Visualization, Python for Data Science

Focus on advanced courses and projects that relate directly to data science and showcase your technical abilities. Leave off introductory or general education classes.

3. Include data science certifications

Data science certifications show your expertise in specific tools and techniques. If you have earned any certifications, include them in your education section.

Some data science certifications to highlight:

• IBM Data Science Professional Certificate
• Google Data Analytics Professional Certificate
• Amazon AWS Big Data Certification

Only include certifications from reputable organizations or companies. Online course certificates are usually not substantial enough to include, unless they are advanced or well-known in the industry.

4. Streamline education for senior data scientists

If you are a senior data scientist with years of experience, your education section should be short and to the point. Hiring managers will be more interested in your professional achievements.

• Ph.D. in Statistics, XYZ University, 1995 Dissertation: A Novel Approach to Time Series Analysis M.S. in Applied Mathematics, XYZ University, 1992 B.S. in Mathematics, ABC College, 1990 Graduated Summa Cum Laude

Instead, just include your degrees and schools. Leave off the years if you graduated more than 10-15 years ago to avoid potential age discrimination.

• Ph.D. in Statistics, XYZ University M.S. in Applied Mathematics, XYZ University B.S. in Mathematics, ABC College

  Skills

Your skills section is one of the most important parts of your entry-level resume. It's where you showcase your abilities and qualifications to potential employers. Here are some tips to help you write a strong skills section that will catch the eye of hiring managers and help you land your first job.

1. Highlight relevant skills for entry-level roles

When you're applying for entry-level positions, it's important to focus on the skills that are most relevant to the job you're seeking. Look at the job description and identify the key skills and qualifications the employer is looking for. Then, make sure to include those skills in your resume.

For example, if you're applying for an entry-level marketing position, you might include skills like:

• Social media management
• Content creation
• Email marketing

By highlighting the skills that are most relevant to the job, you'll show employers that you have what it takes to succeed in the role.

2. Use industry-specific language

When listing your skills, it's important to use language that is specific to your industry. This shows employers that you have a deep understanding of the field and are familiar with the terminology and concepts used in the industry.

For example, instead of simply listing "programming" as a skill, you might say:

Programming Languages : Python, Java, C++ Web Development : HTML, CSS, JavaScript Databases : SQL, MongoDB

Using industry-specific language will help your resume stand out and demonstrate your expertise to potential employers.

3. Organize skills into categories

To make your skills section easy to read and navigate, consider organizing your skills into categories. This is especially important if you have a wide range of skills or if you're applying for a technical role.

For example, if you're applying for a data analyst position, you might organize your skills like this:

Analytics : Data mining, statistical analysis, predictive modeling Programming : R, Python, SQL Data Visualization : Tableau, PowerBI, Matplotlib

Organizing your skills into categories makes it easy for employers to quickly see your qualifications and expertise.

4. Optimize for applicant tracking systems

Many companies use applicant tracking systems (ATS) to screen resumes before they even reach a human recruiter. These systems scan your resume for keywords related to the job description. If your resume doesn't contain the right keywords, it may get filtered out before anyone even sees it.

To optimize your skills section for ATS, make sure to include the exact skills and qualifications listed in the job description. For example, if the job requires proficiency in a specific software program, make sure to list that program by name.

Avoid vague or generic skills like: Computer skills Programming Design

Instead, be specific: Adobe Photoshop Java programming UX/UI design

Skills For Entry Level Data Scientist Resumes

Here are examples of popular skills from Entry Level Data Scientist job descriptions that you can include on your resume.

• Data Mining
• R (Programming Language)
• Python (Programming Language)
• Statistical Modeling
• Apache Spark
• Predictive Modeling
• Natural Language Processing (NLP)

Skills Word Cloud For Entry Level Data Scientist Resumes

This word cloud highlights the important keywords that appear on Entry Level Data Scientist job descriptions and resumes. The bigger the word, the more frequently it appears on job postings, and the more likely you should include it in your resume.

How to use these skills?

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• Data Scientist Resume Guide & Examples for 2022

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Entry Level Data Scientist Resumes

• Template #1: Entry Level Data Scientist
• Template #2: Entry Level Data Scientist
• Template #3: Entry Level Data Scientist
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• Template #5: Data Scientist
• Template #6: Data Analyst
• Skills for Entry Level Data Scientist Resumes
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• Entry Level Data Scientist Interview Guide
• Entry Level Data Scientist Sample Cover Letters
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10 Entry Level Data Scientist Resume Examples for 2024

Landing your first job as an entry-level data scientist means presenting a resume that shows your skills clearly. This article guides you through crafting a resume that hiring managers will notice. Find examples and tips to highlight your data analysis, programming, and statistical skills. Learn how to organize your qualifications, projects, and education to start your data science career.

• 12 Apr 2024 - 1 new resume template (Data Insights Analyst - Retail Industry) added
• 04 Apr 2024 - 1 new section (Showcase achievements, not tasks) added
• 27 Mar 2024 - 1 new resume template (Machine Learning Engineer - FinTech Industry) added
• 19 Mar 2024 - 1 new section (Essential technical skills list) added
• 11 Mar 2024 - 1 new resume template (Junior Data Scientist - HealthTech Industry) added
• 03 Mar 2024 - 1 new section (Quantify your impact with numbers) added
• 24 Feb 2024 - 1 new section (Shape your resume for the job) added
• 16 Feb 2024 - 1 new section (Beat the resume screeners) added
• 08 Feb 2024 - 4 new sections, including 'Displaying quantitative skills', added
• 08 Feb 2024 - 5 new resume templates, including Senior Data Analyst, added
• 06 Feb 2024 - Article published
• 03 Feb 2024 - Resumes approved
• 01 Feb 2024 - First draft complete

  Next update scheduled for 20 Apr 2024

Here's what we see in standout entry-level data scientist resumes.

Show Impact With Numbers : The best resumes show clear impact with accuracy improvements , time savings , cost reductions , and efficiency gains . These metrics help you show the value you can bring to the role.

Match Skills To The Job Description : Include skills you have that match the job description. Highlight tools or techniques such as Python , R , SQL , Machine Learning , and Data Visualization .

Current Industry Trends : Stay updated with trends such as automated data cleaning . Show that you're ready to adapt and apply these in your role.

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widget 2: red / but not serious Here's a short quick tip / warning for people to include. If your symptoms get worse or do not improve after 1 day, go to a lower altitude if you can. Try to go around 300 to 1,000 metres lower.

helpful blue / but not serious Here's a short quick tip / warning for people to include. Here's a short quick tip / warning for people to include. Here's a short quick tip / warning for people to include. Here's a short quick tip / warning for people to include. Here's a short quick tip / warning for people to include. Here's a short quick tip / warning for people to include. Here's a short quick tip / warning for people to include.

Entry Level Data Scientist Resume Sample

Education placement on resume.

As an incoming data scientist, it's crucial to leverage your educational experience. Place your educational background at the beginning of your resume. This showcases your strong theoretical knowledge base, particularly important for entry-level positions where you may lack extensive professional experience.

Include relevant coursework that might differentiate you, such as advanced statistics, machine learning, or data visualization. Also, highlight any thesis or significant projects related to data science. This will provide a practical context for your theoretical knowledge.

Displaying quantitative skills

An entry-level data scientist role requires a strong mathematical and statistical foundation. Ensure to include quantitative accomplishments in your resume. Did you manage to improve a process or identify a key insight during a school project? Quantify these achievements. It shows you can not only handle data, but also draw valuable conclusions.

Data scientists often require proficiency with specific tools such as Python, R, and SQL, or data visualization platforms like PowerBI or Tableau. Showcase your familiarity or proficiency with these tools prominently to attract the attention of hiring managers.

Keeping your resume concise

For entry-level data scientist positions, it's most effective to keep your resume to one page. It presents a clear and focused snapshot of your abilities, indicating your aptitude for the meticulous, precision driven role of a data scientist. It also shows respect for hiring managers’ time, who often sift through dozens, if not hundreds, of resumes.

If you find your resume stretching beyond one page, review your content ruthlessly. Prioritize recent and relevant educational and work experience, and cut older or less relevant information. It's about showcasing quality, not quantity, of your experience.

Data Science Associate Resume Sample

Showcasing problem-solving skills.

Problem-solving is at the heart of being a data scientist. Illustrate your knack for problem-solving by sharing instances where you've identified complex problems, dissected them, and formulated effective solutions. Even scenarios from academic projects or hackathons can illuminate your abilities.

Additionally, communication is key in data science, as findings often need to be translated to non-technical teams. Highlight any experience or activities where you had to present complicated information in a clear, understandable way. This shows you not only understand data, but can also help others understand it as well.

Entry Level Data Scientist with Data Visualization Specialization Resume Sample

Beat the resume screeners.

When you apply for an entry level data scientist role, your resume may first be read by a computer program known as an Applicant Tracking System (ATS). It's important to format your resume in a way that this system can read it well. This means avoiding images or graphics that contain important text and using standard section headings like 'Work Experience' and 'Education'.

Here are some key things to keep in mind to help your resume get past the ATS:

• Use relevant keywords from the job description. For example, include skills like 'data mining' or 'machine learning' if they match your abilities.
• Make sure your layout is simple. Use clear headings and bullet points to list your skills and experiences.

These steps will help ensure that your resume is ATS-friendly and that it will be seen by a hiring manager for the entry level data scientist positions you are applying for.

Marketing Associate to Entry Level Data Scientist Resume Sample

Shape your resume for the job.

You want to show you fit the data scientist role you're after. Think about what skills and experiences are most important for someone who works with data. Match your resume to these needs. This tells me, as a hiring manager, that you understand the job and are ready to step in.

• Put your most relevant data projects first. Use terms like data analysis , machine learning , and data visualization .
• Show how you solve problems. Mention a time you used statistical methods or predictive models to make decisions.
• If your past work was not in data science, show matching skills. For example, if you used Excel for data tracking, that’s useful.

Senior Data Analyst Resume Sample

Showcase achievements, not tasks.

When you are crafting your resume as an entry level data scientist, remember that it’s more compelling to emphasize your achievements rather than listing your daily responsibilities. Make sure you convey the value you have added in your experiences. This tells employers what you might bring to their team.

Consider the following shifts from responsibilities to accomplishments:

• Instead of 'Wrote code for data analysis', you could say 'Developed and optimized data analysis code, reducing data processing time by 20%'.
• Rather than 'Assisted in data collection', express this as 'Collaborated in a data collection initiative that increased the dataset accuracy by 15%, leading to more reliable insights'.

These changes help you stand out by providing measurable results that you have achieved. Numbers and outcomes talk louder than tasks, giving a clearer picture of your capability.

Junior Data Scientist - HealthTech Industry Resume Sample

Essential technical skills list.

If you're stepping into the data science field, your resume needs to show your technical prowess. Focus on the skills that will make you stand out as an entry-level data scientist. Here's a list of skills you might include, depending on the job you want:

• Python or R for data analysis and modeling
• SQL for database management
• Machine Learning techniques
• Data Visualization tools like Tableau or PowerBI
• Big Data platforms such as Hadoop or Spark
• Statistical analysis abilities
• Data wrangling skills with Pandas or NumPy
• Git for version control

You don't need to list every skill, just those that match the data science role you're aiming for. For example, if the job focuses on data visualization, emphasize your experience with Tableau or PowerBI . Place these skills in a dedicated section on your resume to help it pass Applicant Tracking Systems (ATS), which employers use to filter candidates.

Remember, show how you've used these skills in real projects or during your education. This helps employers see your practical experience, which is valuable even at the entry level.

Machine Learning Engineer - FinTech Industry Resume Sample

Quantify your impact with numbers.

When you apply for a data science role, you must show the value you can bring. Use numbers to make your impact clear. Numbers help hiring managers see your potential quickly and easily.

Think about your past work or projects. Look for ways you have used data to create value. Here are some ideas:

• How you improved a process - maybe you made a model that increased efficiency by 20% .
• If you worked on a project, did it help make decisions faster? Maybe you cut down the time needed to analyze data by 30% .

Remember, even if you're not sure about exact numbers, you can estimate. Think about how your work changed things. Did it make a system run faster or help save money? For instance, if you created a predictive model, estimate how much it could increase accuracy or reduce errors. Consider metrics like:

• Accuracy increase by 15%
• Error reduction by 25%
• Cost savings of $10,000
• Time savings of 5 hours per week
• Boost in data processing speed by 40%
• Reduction in customer support issues by 50%
• Upgraded 3 major data systems
• Conducted over 100 hours of data analysis leading to actionable insights

Data Insights Analyst - Retail Industry Resume Sample

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With advancement in technology over the years, it has become almost impossible to escape data. As per the US Bureau of Labor Statistics reports, data scientists are projected to grow by 31% .

To bag a decent job in the current market, you need to create a recruiter-friendly resume following updated industry standards. Lucky for you, this guide will provide you a well-rounded source for all those tips

Here are the entry level data scientist resume tips to create the best entry level data scientist resume:

• What is the suitable resume format for entry level data science resume?
• How to organize your entry level data scientist resume with distinct sections?
• How to optimize the header section of your entry level data scientist resume for maximum impact?
• How to write a perfect professional experience section of the data science resume?
• What is the best way to enter educational details in your entry level data scientist resume?
• What are the data science skills you should include?
• How to curate an impeccable entry level data scientist resume summary?

Data Scientist Salary

The salary of a data scientist depends on various factors including place of work, seniority level, location, and so on. However, you can get higher pay by developing your skills and being updated with industry trends.

Here is a list of data scientist salaries based on cities:

Entry Level Data Science Resume Formats

Your junior data science resume may get rejected despite being up to date. It could be because it did not get past the ATS, which most companies have as of late. Along with including all of your professional details, you must ensure that your data science resume is ATS-friendly.

To hold together your details together, you need to pick the most suitable resume format.

Also read : How do I make sure my resume is ATS-compliant?

Chronological Format:

This format has a time based approach, highlighting most recent experience first. It gives a clear insight into your career trajectory.

Along with being recruiter-friendly, it is also ATS-friendly because of it's error-free structure. It also shows that the candidate has nothing to hide, so if you have gaps in your resume, you might want to skip this one.

Functional Format

It is used by professionals who changed their industries and by those who have gaps in their professional experience timeline. However, it has the con of being non-ATS-compliant.

Combination Format:

You can opt for resume format if you have vast experience in your field of work. This format allows you to broadly highlight your skills as well as describe your work experience

Also read: How and when to use the correct resume format?

Organize Your Entry Level Data Science Resume

An ideal resume should contain all the distinct sections that can make the resume ATS-compliant as well as recruiters-friendly.

The following is a list of the traditional resume sections:

• Personal Information
• Profile Title
• Summary/Objective
• Professional Experience

You can provide the details of the following to further explore your professional and academic achievements:

• Certifications (if any)
• Awards & Recognition (if any)
• Additional Information (if any)

Also read: How to divide a resume into different sections?

Optimize Your Data Science Resume Header

The topmost section of your data science resume is an ideal segment to label your resume and provide your personal information.

Entry Level Data Scientist Resume: Header

Your junior data science resume needs to be labeled with your name to distinguish yourself from the other applicants.

Follow the given tips to frame a flawless resume header:

• Write your resume header in the largest font size of 16-20 points .
• Leave a single space between your first name and last name.
• If you have a middle name, write only the initial of your middle name followed by a period.

Entry level data scientist resume sample for header:

Entry Level Data Scientist Resume: Personal Information

You should provide the following details in an ideal personal information section of your data science resume:

Updated Contact Number

Professional email address, current location.

• GitHub/Kaggle link

While giving out the contact number you need to make sure that it is correct and active so that the recruiters can easily reach you.

Some recruiters may want to interview you over the phone or simply call you up to get a confirmation for any face to face meetings.

Tips to write the contact number on data science entry level resume:

• Always mention your personal number, not your parent's or someone else's.
• Write the country ISD code in front of your contact number and use a plus(+) sign before the ISD code

Avoid giving out any email address that has a made-up name because it is not considered professional. Your email address should always have your real name.

• johndoe27@gmail[dot]com
• john.doe@gmail[dot]com
• doe.john03@gmail[dot]com
• iamj0hn3283@gmail[dot]com

If you are considering a job in some other country other than yours then you can mention your location as city, country code

Else simply mention your location as city, state code.

There is no need for you to mention your personal home address in your entry level data science resume template so make sure that you avoid unnecessary details like your house number, street name, etc.

Social Platform Links

As an entry level data scientist, the candidate must have experience in conducting academic projects. Such projects give a glimpse of the candidate's skills to the hiring manager through GitHub or Kaggle.

Aside from that, you can also include your LinkedIn profile, if it is updated and active. It will help recruiters explore your candidacy and make sure if you are the right fit for the job.

A lot of recruiters prefer candidates who give their LinkedIn profile because it allows a lot of room to review their application, more than other applicants.

Data science resume sample for contact information section:

Also read: How to compose a crisp contact information section?

Entry Level Data Scientist Resume: Profile Title

Your profile title is the representation of your professional status in a resume and needs to be accurate at all times.

The profile title in your data science resume can communicate the following facts to the recruiters:

• Your current designation.
• Your functional industry.
• Your level of professional seniority.

Here is what you need to do while framing your profile title in a resume for data scientist:

• It should be the second-largest text in the resume after your resume header.
• It should ideally be framed in the 14-16 font size.

Here is a snapshot of an ideal profile title from our entry level data scientist resume template:

Perfect Your Entry Level Data Scientist Resume Professional Experience

The professional experience section is one of the most important sections of your data science resume. Since a lot of companies use the ATS, you must make sure that your resume writing standards are updated and recruiter-friendly.

Here are some tips you can follow:

Framing Points:

• You need to break down your work record in one-liner entry level data scientist resume points
• Start your points with a power verb to discuss your work history
• Validate your achievements by mentioning achievement figures

Grouping & Highlighting:

• Create different headings and list all the similar points under relevant subheadings
• Select words or phrases that throws light into your productivity and bold them

Doing so can make the recruiters acknowledge your professional involvement in executing the roles and responsibilities assigned to you.

Also read: How to craft a job-winning professional experience section?

Look at the ideal entry level data scientist resume example of the professional experience section presented through our data science resume sample given below:

Include Your Educational Details in Your Entry Level Data Scientist Resume

A data science resume is incomplete without the education section.

To be a Data Scientist you need to have a Bachelor's degree in Computer science, Social sciences, Physical sciences, and any relevant field of study. If you have more than a Bachelor's degree make sure to mention the same.

Here is a list of the details that you need to provide:

• The name of the school/university
• The location of your school/university.
• Joining and graduation dates in the mm/yy format
• Relevant course modules

There is an advantage of providing your educational details, especially if you are writing an entry level data scientist resume. Your lack of experience can be covered with the educational qualification that you hold.

To further help you get a clear picture of an ideal education section of a resume, here is a snapshot of our entry level data scientist resume sample:

Entry Level Data Scientist Resume Certifications

Get the attention of the recruiters by providing the details of your certification(s).

If you are a certified data scientist, it can have a positive impact on your job application and make the recruiters give you extra credit.

The certifications section of your data science resume can help you communicate the following details about you to the recruiters:

• Certification course name.
• Name of the institute of affiliation.
• Completion date of the course in the mm/yy format.

Here are a couple of entry level data science certifications:

• Cloudera Certified Associate (CCA) Data Analyst
• Cloudera Certified Professional (CCP) Data Engineer
• Data Science Council of America (DASCA) Principle Data Scientist (PDS)
• Dell EMC Data Science Track (EMCDS)
• IBM Data Science Professional Certificate
• Microsoft Certified: Azure Data Scientist Associate
• Open Certified Data Scientist (Open CDS)
• SAS Certified Data Scientist

Here is an entry level data science resume example showcasing the ideal certifications section for your data scientist entry level resume:

Entry Level Data Science Resume: Additional Information

There are various details that you can mention in your junior data scientist resume.

For example, if you have the ability to speak or write more than one language you should mention the same in your junior data scientist resume.

Doing so can make you stand out as an applicant who has the ability to communicate with others who speaks a different language.

Apart from these, you can always provide the details of any extracurricular activities that you have been involved in to emphasize your experience in creative learning, especially as an entry-level applicant.

Such extra details will help you in communicating your soft skills effectively and be considered as an active student and professional. However, it would be a waste of space if you are an experienced professional, because at that point recruiters care more about hard skills.

Entry Level Data Science Projects for Resume

Including your academic projects in your data science resume is a good way to attract recruiters' attention. It helps the recruiters gauge your skills and work experience which will help you stand out from the crowd.

Here are the steps to write a perfect data science projects for resume section:

• Give a short project description
• Mention the tech stack of the project
• List your roles and responsibilities along with some of the accomplishments of the projects with figures

EXAMPLE : Project: Readme Analysis | Tech Stack: Tableau, Scikit-learn, SQL

• Deployed NLP to understand the programming language used in the repo with 89% accuracy
• Employed an API to transcode the programming language into a read me file for each report

Present Your Entry Level Data Scientist Resume Skills

The skills section is another important segment of your data science skills resume wherein you can inject suitable keywords that can help your data science resume get past the ATS.

Go through the professional experience section of your best entry level data scientist resume and pick the skills that elucidate your professional caliber.

Avoid cramping up the skills section with phrases as it may affect the effectiveness of your data science skills resume.

The whole point is to make the skills section crisp and make the recruiters recognize your potential as a data scientist.

Another thing you need to do while farming your skills in your junior data science resume is to create separate sections to highlight your technical and functional skills.

Here are some of the data science resume key skills:

Technical Skills:

Here is a data science resume example to help you see what an ideal skills section should look like in a data science resume:

Curate an Impeccable Overview of Your Entry Level Data Scientist Resume

What goes in the first half of your resume depends a lot on your level of experience. You can either frame a resume summary if you have enough years of experience, or draft a resume objective if you are an entry-level professional.

Entry Level Data Scientist Resume Summary

A suitable resume summary for entry level data scientist should include some of the distinct professional experience that you have acquired over the years and also highlight your core skills.

Here is a list of all that you should do to compose your resume summary for entry level data scientist:

• Write your summary at the end of resume-writing process as you will have a well-rounded mental overview of your work history.
• Pick the highlights of your career from the work experience section of your resume.
• Unless you have 10+ years of extensive work experience try not to exceed your resume summary to more than 3-4 lines.
• Start your sentences with power verbs and make sure that you maintain a cause-effect methodology.

Refer to the data scientist resume example for an ideal entry level data scientist resume summary:

Entry Level Data Science Resume Objective

Give an overview of your data science entry level resume by composing a resume objective. You should include an entry level data science resume objective if:

• You do not have any work experience.
• You have less or limited work experience below 3 years.
• You are a fresh graduate writing an entry level data scientist resume.

Instead of asking what the organization can provide you, rather examine what you can contribute to the organization, which is what you should include in an objective.

The ideal resume objective should be able to communicate your willingness to learn from the roles and responsibilities that would be given to you, along with highlighting your skills as a professional data scientist.

Entry Level Data Scientist Sample Resume

Before we go ahead with more about resume-writing, here is our complete data science resume template to help you understand what an ideal resume should look like:

• Software & Website: SAS, Google Cloud Analytics
• Programming Language: Python, R, C, C++
• Enhancing the advanced data analytics for supporting all go-to-market-strategies as part of a 60 member data science team
• Leveraging expertise in data-driven science in B2B commerce to increase intelligence in the go-to-market functionality
• Employing mathematics, statistics, econometrics, and operations research for developing machine learning solutions
• Contributing to diverse fundamental science research programs for studying human health problems like cancer & infections
• Executing independent data science projects with 20 customers for solving specific business problems
• Administering data science projects for configuring the solutions to maximize value for the customers
• Exploring and validating new techniques to incorporate into price segmentation
• Participating in the development, validation, and delivery of reporting tools
• Converting opportunities into product modules that are valuable across multiple industries
• Conducting technical research while leading and mentoring a team of 10 senior data analysts for business development
• Rendered assistance in translating requirements into technical specifications for data and reporting teams
• Translated requirements into design solutions for 20 business leaders , stakeholders, product managers, and internal teams
• Leveraged quantitative skills extensively to clean, transform & interpret raw data for providing data-driven recommendations
• Extracted, processed, and analyzed large data to solve the most pressing analytical issues
• Performed statistical analysis to identify internal performance pattern & devised data-driven strategies to optimize the same
• Improved business by 60% by conducting data analysis and executing projects for driving business recommendations
• Designed, implemented, analyzed, and tested 4 new features to improve the product suite
• Developed 7 end-to-end business intelligence solutions to advance Power BI functionality and features like Power BI server
• Crafted and delivered 6 performance monitoring dashboards to track business performance
• Conceived, built, launched, and maintained 4 dashboards to improve AV operations
• Liaised with 10 colleagues from sales, operations, product and finance teams to deliver solutions for improving operations
• Built 2 reporting and metrics tracking solutions to optimize cash management
• Conceptualized frameworks and quantitative models to seize new business ventures
• Developed 6 dashboards and frameworks to monitor business performance while creating business cases
• Drafted monthly reports on sales performance including operations & profitability to identify opportunities for improvement
• Developed 60 reports on client usage and performance prior to contract renewals to suggest best products for client scenarios
• Formulated and managed 10 execution plans of business intent to monitor results
• Built strategies and improved the profitability of the network by 10% while solving complex business problems
• Interpreted learnings into data pull and visualization for automation while creating metadata specifications for compliance
• Construed business requirements into proposed data definitions for creating 10 policies as per industry standards
• Certified Analytics Professional | Marble Academy | Berkeley, CA | Jul '20 - Dec '20
• Cloudera Certified Associate : Data Analyst | Cloudera | Jan '18
• Languages : English (Native) and Vietnamese (Interactive)

Key Takeaways

Here are some key takeaways from the guide to help you write a job-winning data science resume:

• Always make it a point to draft your data science internship resume in a suitable resume format.
• Label your resume with your name and provide your personal information in line with the hiring guidelines.
• One-liner points have a higher chance than paragraphs to be read by the recruiters and comprehend your work experience statements.
• Provide achievement figures to give the recruiters an idea of what you are capable of contributing to an organization's higher goals.
• Give an overview of your resume by including a suitable data science resume summary .
• Include data science projects to enhance your candidacy and show your expertise.

Go to Hiration's Online Resume Builder and create a professional resume for yourself. Additionally, reach out to us at [email protected] or get 24/7 professional assistance for all your job & career-related queries with our chat support.

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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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Resume Checker Resume Builder

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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How To Create An Impressive Data Science Resume For Entry Level Jobs

Crafting a resume that lands interviews for coveted entry level data science roles can be challenging, especially for recent graduates or career switchers new to the field. This comprehensive guide provides expert tips and examples for developing an impressive data science resume tailored to entry level opportunities.

It covers key sections and content to highlight, formatting best practices, important skills and keywords, and mistakes to avoid.

If you’re short on time, here’s a quick answer: An effective entry level data science resume should showcase relevant coursework, projects, and skills in statistical programming, machine learning, and analyzing large datasets .

Quantify achievements, optimize with key tech/data buzzwords, and highlight both hard and soft skills to demonstrate potential.

Crafting an Impactful Data Science Resume Objective or Summary

When applying for entry-level data science jobs, your resume objective or summary is your chance to make a strong first impression. This section should effectively communicate your skills, qualifications, and career goals to potential employers.

Here are some tips to help you craft an impactful data science resume objective or summary:

What to Include in a Resume Objective/Summary

Your resume objective or summary should be concise yet compelling. It should clearly state your career goals and highlight your relevant skills and qualifications. Here are some key elements to include:

• Your career goals: Clearly state your objective or aspiration in the field of data science. For example, you might mention your desire to apply your analytical skills to solve complex business problems.
• Skills and qualifications: Highlight the technical skills and knowledge you possess, such as proficiency in programming languages like Python or R, experience with machine learning algorithms, and familiarity with data visualization tools.
• Educational background: Include your degree, major, and any relevant coursework or projects. If you have completed any data science certifications or attended relevant workshops, mention those as well.
• Relevant experience: If you have any previous work experience or internships in the field of data science, briefly mention your responsibilities and achievements. Even if you don’t have direct experience, highlight any transferable skills or experiences that demonstrate your ability to excel in a data-driven environment.

Customizing for Specific Data Science Roles

When applying for different data science roles, it’s important to tailor your resume objective or summary to match the specific requirements of each position. Research the job description and company to understand what skills and qualifications they are seeking.

Here are a few tips for customizing your objective or summary:

• Keywords: Incorporate relevant keywords from the job description into your objective or summary to show that you have the specific skills they are looking for.
• Highlight relevant experiences: Emphasize any experiences or projects that align with the requirements of the role. For example, if the job focuses on natural language processing, mention any projects you have worked on in that area.
• Show enthusiasm: Demonstrate your passion for the field and the company by expressing your excitement for the opportunity to contribute to their data science team.

Example Objective and Summary Statements

Here are a couple of examples to give you an idea of how to craft your own data science resume objective or summary:

Objective: Highly motivated data science graduate seeking an entry-level position where I can apply my strong analytical skills and knowledge of machine learning algorithms to solve real-world business problems.

Eager to contribute to a dynamic team and leverage data-driven insights to drive innovation and growth.

Summary: Recent data science graduate with a strong foundation in programming, statistical analysis, and data visualization. Proficient in Python and R, with experience in applying machine learning algorithms to analyze complex datasets.

Passionate about leveraging data to drive strategic decision-making and optimize business performance.

Remember, your resume objective or summary is your chance to make a strong impression and stand out from other applicants. Tailor it to the specific role you are applying for, highlight your relevant skills and experiences, and showcase your enthusiasm for the field of data science.

Highlighting Relevant Data Science Skills and Keywords

When creating an impressive data science resume for entry-level jobs, it is crucial to highlight your relevant skills and keywords that will catch the attention of potential employers. By showcasing your expertise in technical skills, soft skills, and utilizing key buzzwords, you can demonstrate your proficiency in the field and increase your chances of landing a job in data science.

Technical Skills to Include

Employers in the data science industry are looking for candidates with a strong foundation in technical skills. Some essential technical skills to include on your resume are:

• Proficiency in programming languages such as Python, R, or SQL
• Knowledge of statistical analysis and modeling techniques
• Experience with data visualization tools like Tableau or Power BI
• Familiarity with machine learning algorithms and frameworks
• Understanding of big data technologies such as Hadoop or Spark

By highlighting these technical skills, you are showing potential employers that you have the necessary knowledge and tools to excel in the field of data science.

Soft Skills Valued in Data Science

In addition to technical skills, employers also value candidates with strong soft skills. These skills can demonstrate your ability to work effectively in a team and communicate complex ideas. Some important soft skills to include on your resume are:

• Analytical thinking and problem-solving
• Strong communication and presentation skills
• Attention to detail and accuracy
• Ability to work independently and in a team
• Adaptability and willingness to learn new technologies

By showcasing your soft skills, you are demonstrating that you not only have the technical expertise but also the interpersonal skills necessary to succeed in the field of data science.

Optimizing with Key Buzzwords

When crafting your data science resume, it is important to optimize it with key buzzwords that are commonly used in the industry. These buzzwords can help your resume stand out and show that you are familiar with current trends and technologies. Some popular buzzwords in data science include:

By incorporating these buzzwords into your resume, you are showing that you are up-to-date with current industry trends and technologies, making you a more attractive candidate to potential employers.

Remember, creating an impressive data science resume is all about showcasing your relevant skills and keywords. By highlighting your technical skills, soft skills, and utilizing key buzzwords, you can make your resume stand out and increase your chances of landing an entry-level job in data science.

Featuring Academic Projects and Courses

Describing relevant coursework.

When creating an impressive data science resume for entry-level jobs, it is important to feature your academic projects and courses. One way to do this is by describing the relevant coursework you have completed.

Highlight the courses that are directly related to data science, such as statistics, machine learning, and data mining. Provide a brief summary of the topics covered in these courses and any hands-on experience you gained.

This will show potential employers that you have a solid foundation in data science.

Detailing Practical Data Science Projects

In addition to showcasing your coursework, it is crucial to detail practical data science projects you have completed during your academic journey. These projects demonstrate your ability to apply the knowledge and skills you have acquired.

Include a brief description of each project, the techniques and tools used, and the results achieved. Be sure to highlight any unique approaches or challenges you encountered. This will give employers a better understanding of your capabilities and problem-solving skills.

Linking to Project Code and Examples

To further enhance the impact of your data science resume, consider linking to the project code and examples you have developed. This allows employers to see your work firsthand and assess your technical proficiency.

Provide URLs or GitHub repositories where your code and project documentation can be accessed. Additionally, include any relevant visualizations or data analysis outputs that you have created. This not only validates your skills but also adds a visual element to your resume, making it more engaging and memorable.

Remember, when featuring your academic projects and courses, make sure to prioritize those that are most relevant to the position you are applying for. Tailor your descriptions to highlight the skills and knowledge that align with the job requirements.

By showcasing your academic achievements in data science, you can greatly increase your chances of landing an entry-level job in this exciting field.

Listing Work Experience and Leadership

When creating an impressive data science resume for entry-level jobs, it is important to effectively list your work experience and highlight any leadership positions you have held. This section of your resume allows employers to see your practical experience and how you have contributed to previous organizations.

Including Internships and Volunteering

One way to showcase your work experience is by including any relevant internships or volunteering experiences you have had. These opportunities provide valuable hands-on experience in the field of data science and demonstrate your dedication and commitment to learning.

Be sure to mention any specific projects or tasks you were involved in during these experiences, as this will give employers a better understanding of your skills and abilities.

Emphasizing Transferable Skills

While you may not have extensive work experience in the data science field as an entry-level candidate, you can still emphasize transferable skills that are relevant to the role. For example, if you have experience in coding or programming languages such as Python or R, be sure to highlight this on your resume.

Additionally, skills such as problem-solving, critical thinking, and attention to detail are highly valued in the data science industry, so be sure to showcase these skills as well.

Showcasing Leadership Positions

If you have held any leadership positions, whether it be in a student organization or a part-time job, it is important to showcase these experiences on your resume. Leadership positions demonstrate your ability to take initiative, manage teams, and make important decisions.

These qualities are highly sought after in the data science field, as professionals often work in collaborative environments and need to effectively communicate and lead others.

According to a study conducted by LinkedIn, 41% of hiring managers consider leadership experience as a crucial factor when evaluating entry-level candidates for data science positions.

By effectively listing your work experience and highlighting any leadership positions you have held, you can create an impressive data science resume that stands out to employers. Remember to tailor your resume to each job application, focusing on the experiences and skills that are most relevant to the specific role you are applying for.

Formatting and Design Best Practices

Using clear, scannable formatting.

When it comes to creating an impressive data science resume, formatting is key. Hiring managers often receive a large number of applications, so it’s important to make your resume easy to read and scan.

Use clear headings and subheadings to organize your information and make it easier for the reader to navigate. Use bullet points to highlight your skills and achievements, and use a consistent font and formatting style throughout the document.

Remember, you want to make it as easy as possible for the hiring manager to quickly grasp your qualifications and the value you can bring to the position.

Selecting the Right Length

When it comes to resume length, it’s important to strike a balance. While you want to include all relevant information, you also don’t want to overwhelm the reader with a lengthy document. For an entry-level data science resume, it’s generally recommended to keep it to one page, unless you have extensive experience or additional relevant certifications.

Keep in mind that hiring managers typically spend just a few seconds scanning each resume, so it’s crucial to prioritize the most important information and keep it concise and impactful.

Crafting Section Headings Strategically

Section headings are an essential part of your resume’s organization and can help highlight your qualifications and achievements. When crafting your section headings, be strategic in your approach. Use clear and concise headings that accurately reflect the content of each section.

For example, instead of using a generic heading like “Work Experience,” consider using specific headings like “Data Science Internship” or “Research Assistant” to showcase your relevant experience. Additionally, consider using bold or a larger font size to make your section headings stand out and catch the reader’s attention.

Remember, your resume is your chance to make a strong first impression and stand out from the competition. By using clear, scannable formatting, selecting the right length, and crafting section headings strategically, you can create an impressive data science resume that grabs the attention of hiring managers and increases your chances of landing that entry-level job.

For more tips and examples of well-formatted resumes, check out websites like The Muse or Indeed .

In summary, an exceptional data science resume for entry level candidates highlights technical expertise, soft skills, hands-on projects, and a passion for data to stand out. A tailored resume objective, measurable achievements, optimizing keywords, and strong presentation will demonstrate value to employers hiring for data science roles.

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Data Scientist Resume - Sample & Guide for 2024

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

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

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

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

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

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

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

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

How to Format a Data Scientist Resume

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

Now, this is more important than it sounds.

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

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

The following resume formats also get our approval:

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

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

Use a Data Scientist Resume Template

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

The same needs to happen to your resume.

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

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

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

What to Include in a Data Scientist Resume

The main sections in a data scientist resume are:

• Work Experience
• Contact Information

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

• Awards & Certification

Interests & Hobbies

What should you write for each section? 

Read on to learn how.

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

How to Correctly Display your Contact Information

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

The only requirement is accuracy. 

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

The contact information section on your resume must include:

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

How to Write a Data Scientist Resume Summary or Objective

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

Instead, they scan the resume for the main points.

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

So, what can you do?

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

To do this, use a resume summary or objective .

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

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

But what is the difference between the two sections?

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

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

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

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

So, which one is best, summary or objective?

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

How to Make Your Data Scientist Work Experience Stand Out

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

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

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

• Position name
• Company Name
• Responsibilities & Achievements

Data Scientist

03/2016 - 05/2019

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

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

Instead of saying:

“Data streaming.”

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

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

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

What if You Don’t Have Work Experience?

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

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

Your experience is null .

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

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

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

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

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

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

Use Action Words to Make Your Data Scientist Resume POP!

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

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

• Conceptualized
• Spearheaded

How to Correctly List your Education

Every great resume needs an education section.

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

Simply enter your education history in the follow format:

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

BSc in Statistics

University of Bath

2012 - 2016

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

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

• What if I haven’t finished education yet?

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

• Should I include my high school education?

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

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

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

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

Top 15 Skills for a Data Scientist Resume

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

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

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

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

Here are some of the most common data scientist skills:

Hard Skills for a Data Scientist Resume:

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

Soft Skills for a Data Scientist Resume:

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

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

What Else Can You Include in a Data Scientist Resume?

We’ve now covered every essential resume section .

Is it the absolute BEST it can be?

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

Awards & Certifications

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

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

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

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

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

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

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

Rank the languages by proficiency:

• Intermediate

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

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

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

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

Include a Cover Letter with Your Resume

Here the thing –

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

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

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

Here’s what we recommend:

You should complete the following sections:

Personal Contact Information

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

Hiring Manager’s Contact Information

Full name, position, location, email.

Opening Paragraph

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

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

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

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

Closing Paragraph

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

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

Formal Salutations

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

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

Key Takeaways

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

Let’s quickly summarize what we’ve learnt:

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

Suggested Reading:

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

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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:.

Entry Level Data Scientist Resume Examples & Writing Guide

The Entry-Level Data Scientist’s Resume Guide for 2024

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

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

• How to properly format your resume for success in 2024
• How to talk about your work experience and personal projects
• How to create a competitive skills section
• How to customize your resume for each job you apply to

Formatting a Entry-Level Data Scientist Resume

Reverse chronological structure: Recent experience first

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

Entry-Level Data Scientist Resume Length

Stick to 1 page.

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

Avoid Common Entry-Level Data Scientist Resume Format Mistakes

1) Resumes must include as much content as possible.

2) Uniform and labeled  section headers

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

3) Minimal white-space

8 ATS IT Resume Examples (Including Writing Tips)

Key Sections for a Entry-Level Data Scientist Resume 

A tailored resume summary .

In a competitive field like Entry-Level Data Scientist, hiring managers sometimes review hundreds of applications for a single position. Writing a resume that stands out is difficult, so solve that problem by including a compelling introduction for your resume. Begin your resume with a resume objective, which is a 2–3 sentence paragraph about your job-relevant skills and career goals.

    Entry-level Data Scientist with 2 years of experience in Python and R for large scale data collection, analysis and reporting. Proven understanding of predictive analytics with existing tools. Applying for a position at Company A to apply knowledge of data analytics to improve business decision making.

     Entry-level Data scientist with 2 years of extensive experience in Java, NoSQL, and Apache Spark. Proficient in web scraping and data cleansing. Proven ability to build and deploy scalable data pipelines. Candidates for Data Scientist role at Company A should possess the following qualities: curiosity, passion for building things, and ability to lead cross-functional teams.

     Entry-level Data Scientist with 3 years of experience in Data Science and Data Analysis. Proven ability to implement and test algorithms using SQL and Python. Outstanding writing and presentation skills. Seeking to leverage data lineage to drive product innovation and growth at Company A.

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

Your Relevant Professional Experience 

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

However, you should also be including any positive results you’ve accomplished.

Information Technology(IT) Certifications

Emphasize your credentials. Include mention of all Important Skills for Information Technology (IT) Jobs , Certified Analytics  Professional(CAP), Cloudera Data Platform Generalist Certification, Data Science Council of America(DASCA) Senior Data Scientist, IBM Data Science Professional Certificate, Microsoft Certified : Azure AI Fundamentals, Open Certified Data Scientist(Open CDS) or other IT/Programming methods or processes.

Skills for a Entry-Level Data Scientist Resume

Either at the beginning (after the resume summary) or at the end of your resume, insert a tech table that lists the software, hardware, and technical processes with which you are conversant. Be sure to include those that are specifically mentioned in the job ad; these are important “keywords” that will help rank your resume highly when it is scanned by an automated applicant tracking system during its first round of review.

Hard skills like software, tools, and the technical knowledge to interact on websites, developing servers, and databases for website functionality, and coding for different platforms. But don’t forget to include soft skills like teamwork and communication skills, which are necessary to coordinate with a developer, Consultant, Business owner and ui/ux designer. 

Here are more key hard and soft skills that look great on a Entry-Level Data Scientist resume:

Hard skills to consider

• Engineering programs: Python, C/C++, Java, Perl, SQL, R, SAS, Hive, Spark, Big Data, Statistics, Mathematics, Deep Learning, Machine Learning, Data Visualization, Processing Large Data sets, Data Wrangling
• Industry credentials: Certified Analytics  Professional(CAP), Cloudera Data Platform Generalist Certification, Data Science Council of America(DASCA) Senior Data Scientist, IBM Data Science Professional Certificate, Microsoft Certified : Azure AI Fundamentals, Open Certified Data Scientist(Open CDS) 

Soft Skills to consider

• Communication
• Product Understanding
• Adaptability
• Critical Thinking
• Logical Reasoning

IT/Programming Resume Content

Using metrics to quantify experiences.

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

For instance, list how you Implement data structure measures. How you design, develop and maintain computer software also build systems that collect, manage, and convert raw data into usable information to interpret. How you design user interactions on websites, develop servers, and databases for website functionality, and coding for mobile platforms.

Optimizing Resume Content with IT/Programming Keywords

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

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

Instantly Generate Entry-Level Data Scientist Resume Content

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

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

• Experience level

Learn more about Rezi AI Resume Writer here

Include A Entry-Level Data Scientist Cover Letter

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

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

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

Learn more about Rezi AI Cover Letter Builder here

Our most Recommended resume templates

Professional, clean, effective. These templates get your message across, no matter your industry or experience level.

Ultimate readability and well-organized layout. Highlights what matters the most. A safe pick for all jobs across all industries.

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

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

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

Frequently Asked Questions (FAQs)

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

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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: Downloadable Sample & Real Examples

As a Data Scientist, you help organizations and companies make the correct decisions based on key analysis. Now it’s your turn to receive expert advice. With our professional template, you can easily create a resume that will get your next job!

Data Scientist Resume Example MSWord® Download our free Data Scientist Resume Template in Word, and the probability of getting hired will go through the roof.

Data science is already one of the United States’ top careers, but it’s still true that data science will grow to be a tremendously high-demand profession. 

To be ahead of the curve, you need to have an effective and valuable data scientist resume. 

If you’re pursuing a job in a competitive industry like data science, not only do you need to show you’re qualified, you also have to stand out from the rest.

This ResumeGiants guide explains:

• How to build a data scientist resume that fulfills all of your employer’s requirements
• What to anticipate and understand regarding data scientist credentials for resume writing
• Using a job description for data scientist resume optimization
• How to highlight your soft and hard skills for a data scientist resume
• Analyzing a sample data scientist resume and data science resume template as well as how to utilize it as a reference guide
• How to differentiate between data scientist resume samples for your benefit so you know what works and what doesn’t work 

Data Scientist Resume Sample

Feel like you need additional assistance organizing the structure, material, or organization of your resume? 

Check out our data scientist resume template , which we’ve designed to present you with a comprehensive image of what an excellent resume should look like.

[ Mark Winklevoss ]

[ Microsoft-Certified Data Scientist ]

24 Palo Alto, San Francisco, CA |   252-927-8254   |   [email protected]

Data Scientist with 8+ years of expertise implementing statistics-based solutions to improve data storage processing efficiency, consistency, and applicability. Executed action-oriented approaches to complex organizational challenges and created data prediction models, used to analyze cryptocurrency trends. Seeking to apply at XYZ to improve deep statistical learning and data-related solutions.

Data Scientist

Microsoft, San Francisco, CA

2016 – 2021

• Executed data-mining study of the correlation between crypto asset prices and industry developments, resulting in a 19% more accurate performance forecast than prior years
• Utilized Python algorithms to gather and organize competition data. Redesigned data visualization techniques to include quantitative graphs and visuals to increase user interaction
• Analyzed blockchain fragmentation and data recall to predict non-fungible token prices
• Updated database optimization methods to decrease redundancy by 15%

Infosys, San Francisco, CA

• Improved data mining methods, which resulted in 10% reduction in time to derive consumer data insight utilized to design marketing plans
• Modernized encryption, image compression, antivirus security, and encrypted remote monitoring processes to improve data security
• Created AI platform to scale social media reach and marketing for Australian Open with Infosys as data partner

MS Statistics

Stanford University, CA

2011 – 2013

• Honors: Graduated magna cum laude with a 3.8 GPA.
• Relevant Coursework: AI Programming, Blockchain Development, Regression Models, Machine Learning
• Soft skills: Communication, Critical Thinking, Problem Solving, Risk Analysis
• Hard skills: Data Analysis, Machine Learning, Modeling, Quantitative Analysis, Python, SQL, TensorFlow, Cloudera
• “Employee of the Year,” Machine Learning Division, Infosys (2015)

Certificates

• GCP – Google Certified Professional Data Engineer (2017)
• Microsoft Professional Program Certificate in Data Science (2019)

Free data scientist resumes and templates aren’t easy to find on the internet.

So, don’t hesitate to make use of our data science resume sample template above and the other information we’ve put together for you below if you’re looking to craft the perfect data scientist resume.

What’s the Best Data Scientist Resume Format?

If you want to crack the code for making the perfect resume, you’ll have to start off by choosing a resume format that suits your specific niche and needs.

It’s vital first to examine your degree of expertise and the content you wish to present on your resume.

Unless you have many years of experience, particularly the sort that can organically fill multiple pages, your resume does not need to be any longer than a page in length.

Below is some of the most important information you want to have on your resume:

• Personal information and contact information
• A resume objective or resume summary
• Your work experience, past job details and qualifications
• Your soft skills and hard skills
• Any extra material that demonstrates your fit for the job

A reverse-chronological structure is the industry standard for most professions. A data scientist resume is no different.

A few more things you should consider when creating your data scientist resume format are as follows:

• Keep it simple. A font should never be the element overpowering your resume. A font style that is readable, clean, and tidy should do the job.
• This is the standard amount of spacing you want to have. Anything more will seem like there’s too much space between lines, and anything less can make your resume look too cramped.
• The same logic applies here. Your resume should not only have space to breathe between the text but from the sides as well. Be flexible with white space to avoid your resume looking cluttered.
• Using bold headings helps demarcate each section on your resume and makes it look more organized. It also helps a recruiter identify different parts of the resume when navigating through it.
• Having a resume longer than one page is acceptable; skipping essential things about your job isn’t. If you feel there are things you must have on your resume, it’s probably worth adding them at the expense of having a one-page resume.
• When applying for a job as a data scientist, it’s generally ill-advised to include images or pictures of any sort. At most, you can include a small, professional photo of yourself in one of the top corners, but even this is not necessary. You’re likely much better off using the space for more valuable information like your skills or qualifications.
• Unless the job description says otherwise, using PDFs is the way to go. It’s the unofficial gold standard in this industry and maintains consistency for everyone. When you send a PDF, rest assured your resume will be viewed exactly as you intended it to be.

When it comes to formatting a resume, even the most accomplished data scientists need a bit of help sometimes. 

That’s why we’ve designed tons of interactive, easy-to-use resume templates , all of which you can use to craft your perfect data scientist resume. 

Taking a look at a sample data science resume like the one in this guide can also help you understand how to break up and organize your written material.

How to Write a Data Scientist Resume Summary or Resume Objective

At the beginning of your resume, insert a professionally written data scientist resume headline: either a resume summary or resume objective . 

This is essentially an introductory paragraph of about 50 words about yourself outlining why you’re the right candidate for this position. 

You could call it the “elevator pitch” of your resume or a representation of what’s to come in the rest of your resume.

If you have years of experience in the industry that you feel is relevant, it’s likely best for you to write a resume summary .  Share your most remarkable achievements.

If you’re writing an entry-level resume for a job as a junior data scientist, it’s probably wiser to opt for a resume objective.  

In your resume objective, be sure to explain everything you’ve learned so far and illustrate how well you’d fit in.

Whichever you end up choosing, remember to make your key points not just about your top achievements but also what your employer is looking for.

Demonstrate how your expertise and skill set would convert into something valuable and beneficial for them.

Imagine you’re getting into an elevator with a recruiter from the company you’re looking to apply for. They know of your existence for those thirty seconds. After that, they’ll just walk out and completely forget you – unless you can make an impression . This is basically your resume summary or objective. You have a few seconds – or sentences – to make them take notice of you. Make the best of it!

Data Scientist Resume Summary Example

A good resume summary will let an employer know about you as a person and as a worker. It allows your skill set and personality to shine through in just a few sentences.

To meet this goal, focus on the things that matter the most. Specific examples and precise details of noteworthy things should be emphasized. 

Below, we’ve illustrated how both a poor and strong data scientist resume summary can come across. Focus on what works here – both obviously summarize the individual as a data scientist, but which does it better?

One merely summarizes, while the other really tells about the data scientist in question, their personality, and their skillset, to a certain degree.

Getting off to the right start can convince a recruiter to keep their eyes on your resume longer. A strong data scientist resume will have them interested. 

Your resume should make them want to know more.

Senior data science specialist with an excellent working understanding of Java, Scala, Hadoop, Python, and SQL. Seeking a competitive environment for a data scientist role with autonomous tasks and challenging projects.

Facebook Certified Senior Data Scientist with 12+ years of expertise in Java, Scala, Hadoop, SQL, and Python.  Implemented data mining on the blockchain to assess procurement procedures indicating financial benefits of $600,000 a year. Looking to employ my visualization tooling and crypto forecast modeling abilities to boost XYZ’s market returns in the future year.

How to Write an Entry-Level Data Scientist Resume Objective

As stated earlier, if you don’t have tons of work experience, a resume objective would be better for you. 

That’s because resume objectives do not rely solely on achievements and work experience to demonstrate your worth as a candidate. 

Pay close attention to what the job description says. 

For example, if the job description specifies that the company is looking for applicants who are highly skilled in Python, highlighting that you have these abilities that might make you a great candidate, even if you don’t have years of experience.

Take some time to identify important keywords from each job description that you’re considering. Finding ways to incorporate these major points into your resume headline can help you immediately stand out from the rest.

When writing these resume objectives, it’s good practice to highlight particular projects, operations, coding languages, algorithms, the scale of information, and other data worked with – basically any technical know-how that would impress a recruiting manager.

Look at another example of a value-adding data scientist resume objective:

Big Data Science M.Sc. graduate with 3.6 GPA. Quick learner despite less work experience. Praised for critical thinking and problem-solving skills. Exceptional at locating trends in the blockchain.

While this isn’t a bad example, it doesn’t provide enough information for a recruiter. It doesn’t touch on the specifics , which is what you want to target when writing a resume objective. 

You want to tell the recruiter that you have the exact qualities and skillset they need for this particular role, and that’s why you’re a good fit. 

Here’s another way to write it:

M.Sc. in Big Data with 2+ years of intern experience. Placed first in the 2020 Big Data Science Exhibition for developing an algorithm to predict trends on the blockchain, with 82% success rate. Seeking to incorporate my big data and blockchain abilities to assist enhance crypto modeling forecast accuracy in the next year.

How to Describe Your Data Scientist Experience

Recruiters want to be sure that all your previous experience as a data scientist translates into good results for their company. Your experience section is the major area in which you may exhibit your credentials and achievements .

To communicate these qualities in a manner that enhances your chances of earning an interview, however, you must place emphasis on the precise items your prospective employer values.

If you’re able to link these two things together–what you have with what the recruiter wants–it helps portray you as the most suitable candidate for the position.

Start every bullet point in the job experience section with the appropriate action verbs . Using powerful action verbs is crucial in convincing a prospective employer to read the remainder of your data scientist resume and increases your chances of landing an interview.

Furthermore, utilize action verbs that are unique to your genre of work. Recruiters tend to use application tracking software (ATS) to screen candidates based on the frequency of keywords, phrases, and skills on a resume.

Thesauruses can be a great tool for finding powerful synonyms for verbs and avoiding repetitive or lengthy language. For example, instead of saying you “worked with others” to complete a task, you might say you “collaborated” with others.

Data Scientist Resume Examples: Experience

Many of the tips we’ve discussed so far apply to your experience section. Below are some examples of how you could pen down your work experience as a data scientist to illustrate how they work in action. 

The “wrong” example isn’t necessarily a poor one; you would just be better off going with the latter.

Data Scientist Macrofirm, Inc. 2017 – 2021

• Performed data science for a crypto holding company
• Prepared and submitted models for prospective acquisitions
• Created an algorithm to calculate modified crypto trends
• Prepared and implemented models for prospective acquisitions to fund managers
• Accomplished 14% greater returns on the blockchain successfully in the last quarter
• Developed an algorithm to compute crypto trends and put out graphs accordingly
• Corrected computing procedures that reduced database redundancies by 23%

Entry-Level Data Scientist Resume: Experience Section

If you’re wondering how to get a data science job with a no experience resume , you’ll be happy to hear that you don’t need to have years and years of experience to create a compelling data scientist resume. 

Use the experiences and the skills you picked up along the way (even if it was through other, unrelated jobs) and highlight all the abilities you do have that would be useful for the role you’re applying to.

Here’s an example of an entry-level experience section for a data scientist resume:

Freelance Data Scientist 2015 – 2017

• Placed first in intercollegiate hackathon
• Used algorithms to find data modules
• Regularly contributed to GitHub
• Lacking in real-world experience, but eager to learn with keen interest in data science
• Facebook Hackathon: Placed first among 80+ programmers by finding the right data models in an intercollegiate hackathon
• NinjaNite: Used quickfire algorithms to locate data modules against a timer in a fast-paced environment
• Javalab: Contributed to the Big Data project on GitHub on a regular basis

Is Your Education Section Cracking the Code?

Adding your academic qualifications to a resume is important for more than just demonstrating you’re able to do the job; they can also point out new skills and achievements that aren’t elsewhere on your resume. 

In general, only the highest degrees of your education should be included . So, if you have more than one college degree and are low on space, focus on what you’ve completed most recently.

Here’s how you should list down your education:

• Minors and Majors (if applicable)
• Name of the school
• Date of graduation

If you’re not a seasoned data scientist, don’t fret. Your education can actually help you compensate for any lack of job experience.

If you’re preparing a resume for this purpose, go into further detail about your undergraduate years. Include the following:

• Relevant coursework
• Favored academic disciplines
• Academic honors and accolades
• Activities outside of the classroom

It’s not necessary to include your GPA in your data scientist resume if you have more than a year or two of genuine work experience. If you don’t, it’s still wise to list your GPA only if it’s higher than 3.5. 

Data Scientist Resume Education Section

Here is an example showcasing how you can keep your resume’s education section interesting:

BS in Computer Science, Christ University Springtown Community College | Springtown, IL 2017 – 2021

• Graduated with 3.7 GPA
• Relevant Coursework: Python, Blockchain Development, Regression Models, Machine Learning
• Graduated magna cum laude with a 3.7 GPA
• Excelled in Python coding and data science credit courses
• Completed a thesis on blockchain development using regression models, Python coding, and machine learning
• Vice-President, Coding Club

The Best Data Scientist Skills for a Resume

When it comes to skills for data scientist resumes, it’s important to be thoughtful; these qualifications are pivotal to building an outstanding data scientist resume.

The skills section should be a combination of both soft skills (general skills that apply to most professions) and hard skills (skills that are learned on the job and applied to specific roles).

Try and keep the list to around 5 per skill. Noting down too many can suffocate your resume and make it seem like you’re trying too hard.

Below are some examples of strong data science resume skills that you may want to consider for your own list.

Soft Skills

Communication

Problem-Solving

Critical Thinking

Risk Analysis

Business Intuition

Collaboration

Perceptiveness

Interpersonal Skills

Hard Skills

Data Analysis and Visualization

Programming

Statistics and Probability

Machine Learning

Quantitative Analysis

Python, Java, SQL

OpenRefine/TensorFlow

How to Add Other Sections for an Effective Resume

As a data scientist, “Other” sections can be crucial to differentiate your resume from the rest.

Whether you’re writing a senior data scientist resume or an entry-level resume , finding ways to make your application more individual can go a long way.

Think of this section as the cherry on top. It’s a nice addition to your resume and really highlights all your skills and achievements.

If you’ve taken courses from any bigger companies that offer official certifications, you can add those certification details next to your job title, e.g. John Doe, Microsoft Certified Data Scientist

Data Scientist Resume Sample “Other” Sections

It’s best to stick to a precise, professional tone even for these sections. 

Here’s an example to help guide you and inspire ideas of your own:

• Certficate from Facebook
• Certified Programmer (Microsoft)
• Facebook Ethical Hacking Program Certificate (2016)
• MSCP – Microsoft Certified Professional Programmer (2017)

Below are a few other kinds of sections you can add to your resume . Don’t worry if you’re an entry-level applicant and don’t have any of these – remember that an “Other” section is more of a bonus than a pure necessity.

Don’t be afraid to branch out and include sections of your own beyond this list, though; so long as it’s relevant to your qualifications, it’s possible to make all sorts of experiences work on a resume.

• Conventions
• Certifications
• Accreditations
• Publications
• Industry honors
• Participation in hackathons

Key Takeaways

In this guide, we’ve discovered:

• A resume for a data scientist should have a resume summary or resume objective depending on whether you’re an experienced data scientist or an entry-level applicant, as well as sections for schooling, employment , and other relevant information.
• Powerful terminology (such as action verbs ), detailed examples, and straightforward, precise , and professional wording are all used in strong resumes.
• When it comes to detailing data scientist skills on a resume, be sure to note soft and hard skills.
• Including any certificates, seminars, conferences, honors, or other achievements that can help your resume stand out from the other candidates.

We hope we’ve provided you with enough data to craft the ideal data scientist resume!

If you ever need any additional assistance with your data scientist resume , our easy-to-use resume builder can churn out the ideal resume for you with just a few clicks!

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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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