Data Scientist Resume: Elements, Examples, and Tips

Written by Coursera Staff • Updated on

To write an effective data scientist resume, start with a solid understanding of what the role requires, along with the must-have elements. Learn what to include and how to format your resume with these tips.

{Featured image} Data scientist explains visualized data to a coworker

Data scientists are responsible for some of the most impactful insights that organizations use to guide their decision making. To land such an important position, then, it's necessary that you make a first good impression using your resume.

In this guide, you’ll find helpful tips on writing your resume as a data scientist. We'll explain the education, experience, and skills you should include, as well as discuss job opportunities for data scientists and online courses that may prepare you for your next position.

What is a data scientist?

A data scientist works with data to answer questions and solve problems for a team, company, or organization. Data scientists gather, analyze, process, and model data and interpret the results. Data can be structured, such as dates, credit card numbers, and names or unstructured, such as social media posts, audio files, or surveillance video.

Many data scientists use computer science, social science, and math skills as they work to uncover trends and create solutions. They are both discoverers and problem-solvers. 

Read more: Data Science Jobs: Resources and Career Guide

Data scientist resume: elements and examples

To stand out to employers, your data science resume should be properly formatted and include an overview of your relevant work experience, education, skills, and certifications. Here's what you need to know about each of these different resume elements:

1. Formatting

Your data scientist resume should include the following sections: a header with your name and contact information, summary, work experience, skills, and education. It can also be appropriate to sprinkle in achievements, awards, or professional interests as room allows. Format your resume so that the focus is on those core elements.

When choosing a format, consider using reverse chronological order. This will show potential employers your most recent work history and education. Those items will often be the most relevant information, and a reverse chronological order format makes it easy to find these details. 

Read more: Resume Sections: How to Organize Your Resume

2. Work experience.

Your experience should be the first thing an employer sees after your contact information and an objective or brief summary. It’s a good idea to jump right to experience because, in the field of data science, experience carries a lot of weight. 

When listing your experiences, list them chronologically from your current job or most recent job, back. List relevant experience only. For each position you have held, organize the following pertinent information in this order:

  • Job title

  • Employer's name

  • City and state where the employer is located

  • Your start and end dates for the job (or "present" if you currently hold the position)

  • A bulleted list of your most noteworthy achievements and key responsibilities

If you’re a new graduate with no experience, emphasize the data-oriented projects you've worked on in your academic career or in your personal life. For example, you might note the following experiences:

  • The duties you performed in a data science internship or as a freelancer

  • Contributing to open-source projects like GitHub to build your portfolio 

  • Creating mock projects that you can link to show your skills 

Read more: How to Land a Data Science Internship: Your 2024 Guide

3. Education. 

List your education on your resume, with the most advanced degree listed first. If you don’t have a relevant degree in the field of data science, list your high school education.

You should list your education after your work experience section, unless you're a recent college graduate, in which case you should highlight your degree before your work experience. The reason for this shift is simple: recent graduates lack work experience but possess the training necessary to perform many data science tasks, so by putting their degree first they're emphasizing they have the required skills – even if they lack professional work experience.

To properly format your education history, follow this structure:

  • Degree type, major

  • Name of your school 

  • Years studied

  • GPA

  • Any relevant honors received 

  • Relevant coursework

Here’s a data scientist resume example of how you might list an education entry: 

Bachelor of Science in Statistics

University of Georgia

2012 - 2016

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

  • GPA: 3.7

You can abbreviate your degree (i.e. "B.S") or write it out in full if you have the space (i.e. "Bachelor's of Science"); either is appropriate for a data scientist resume. 


3. Describe your skills.

After listing work experience and education, it’s time to note your skills. List skills in bulleted format for easier readability, and use action verbs when possible.

Include your technical skills, beginning with those you feel are your strongest data science skills as related to the position you're applying for. You’ll want to list both technical and workplace skills. You don’t necessarily need to set them apart but mention both types of skills if they're relevant for the position. 

Read more: How to Feature and Format Key Skills on Your Resume

If you’re unsure which skills to list or what skills are irrelevant, refer to the job description for which you’re applying (or find a sample one online) and match your skills with the ones required for the position or a similar one.

4. List your certifications.

List any certificates you hold that are crucial to the job you're applying for to emphasize your preparedness for the role. This will highlight these essential skills in a way the hiring manager can easily notice.

In your role, you will likely obtain certifications in various programming languages, such as Python, SQL, and MySQL. You might also seek certification specific to the data scientist career field, such as SAS Certified Data Scientist or  Microsoft Certified: Azure Data Scientist Associate. If you have certifications but they are not pertinent to the job yet show you have additional skills that may be helpful to the position, list them under your education section.  

Format your certifications in the following way:

  • Full title of certification and acronym

  • Name of the organization from which you received the certification

  • Date you earned the certification 

Read more: Your Guide to Data Science Certifications in 2024

Data scientist resume tips

Most potential employers only spend a handful of seconds reviewing resumes, so make sure yours stands out. To help craft an eye-catching resume, consider the following tips:

1. Consider the employers’ viewpoint 

Highlight the skills that your employer notes in their job description. Consider common traits employers look for in a data scientist, as well. According to leaders in the industry, these are some of the top skills and attributes employers seek: 

Remember to list both technical and interpersonal skills so that employers can get a well-rounded picture of who you are as an employee and a data scientist. 

If you want to really stand out to employees, consider enrolling in a data scientist certification that can be added to your resume. Certifications show employers that you are hard-working and serious about what you want to do in your career. 

Looking to develop job-relevant skills?

Consider courses offered on Coursera like Machine Learning and Python for Everybody. These are both in-demand skills for any computer scientist and can give you an edge over the competition on your resume. 


2. Create an elevator pitch

An elevator pitch is a short, persuasive summary of why someone should hire you. As you gather your information to write your resume, take the time to jot out a short elevator pitch. This simple exercise will help you identify what’s important and relevant to potential employers. 

On your resume, take a few sentences from this pitch to communicate why you are the right person for the job over others. Describe what makes you unique in the field of data science. Highlight those skills and accomplishments that are most relevant to the position for which you’re applying. What makes you the best candidate for this position? 

3. Tailor your resume to each job

Remember, the purpose of a resume is to land an interview. Be selective with what you include. 

You should generally keep your data scientist resume length to one page if you are a student or recent graduate and two pages if you have experience.

Be concise in your descriptions and include only relevant information. Think about the things that can catch the attention of the employer. Read about the employer, do your research. This act alone can help you know what to highlight and what’s probably not important to them. 

And remember, in data science, projects and work experience are important. Pour your attention into these aspects of your resume. 

4. Keep your format simple

The goal is to create a document that can easily be skimmed through within seconds. Pay attention to whitespace, use bullet points, bold words for emphasis, and break up any large chunks of text. 

A good resume should be clean and easy to read. Avoid designs and a lot of “extras.” It’s important to include proper headers, consistent formatting (i.e., the same font throughout), and some white space. 

Read more: Types of Resumes: Choosing the Right Format For Your Needs

Build data science skills on Coursera

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