Writing a Data Scientist Resume: What to Know

Written by Coursera • 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

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.

When writing a data scientist resume, format your resume in reverse-chronological order, starting with your work experience, followed by education, skills, achievements, and then additional content (professional interests, publications, conferences) if you have space. Include your contact information and a short summary at the top, and keep the length to two pages or less.

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. 

Must-have elements for a data scientist resume

Elements of a well-written data scientist resume include work experience, contact information, 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. 

1. Include your 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 experiences only. For each position you have held, organize the following pertinent information in this order:

  • Title of your position

  • Name of the company

  • City and state where the company is located

  • Your starting and ending dates (or "present" if you currently hold the position)

  • Bulleted list of your most noteworthy achievements and key responsibilities

If you’re a new graduate with no experience, try to build that up first before creating your resume. Some ideas include: 

  • Freelancing in a relevant position or organization 

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

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

2. List your 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. List education after experience since experience takes priority. You can list education first if you are a recent graduate without any experience yet, or with very limited experience. 

Format your education history in the following order:

  • 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 or write it out in full if you have the space; 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 where you can. For example, “proficient in JavaScript.”  

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. 

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 position.

4. List your certifications.

List any certificates you hold that are crucial to the job you're applying for above your experience section. 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, MySQL, and Git. 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.

Continue listing other certifications you hold under the most important ones. 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.  

Add a header that says "Certifications" and list the following information about your credentials:

  • Full title of certification and acronym

  • Name of the organization from which you received the certification

  • Date you earned the certification 

More tips for writing a data scientist resume

Keep your resume concise and informative. Remember that most potential employers spend mere seconds at first glance on a resume, so make yours stand out. Remember also that in the case of a data scientist resume, experience is key. 

Consider the employers’ viewpoint 

Point to your skills that employers like to see. Consider common traits employers look for in a data scientist. According to leaders in the industry, these are some of the top skills and attributes employers seek: 

  • Critical thinking 

  • Coding 

  • Mathematical skills 

  • Machine learning

  • Data architecture 

  • Problem-solving 

  • Communication skills 

  • Teamwork 

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. 

Also, 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. 

Create an elevator pitch

An elevator pitch is a short, persuasive summary of why someone should hire you. You’re essentially selling yourself. 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 prioritize what’s important and relevant. 

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? 



How to Write a Resume (Project-Centered Course)

What you’ll achieve: In this project-centered course*, you will craft an essential cornerstone of the modern-day job or internship search: the resume. ...


(3,700 ratings)

208,539 already enrolled

Average time: 1 month(s)

Learn at your own pace

Be selective about what you include

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. 

Follow a clean, simple format

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. 

Step up your job search with Coursera

Whether you’re just getting started in data science, or you’ve been in the field for years and want to move into a new position, your resume is your golden ticket. 

If you're ready to dig deeper into data science, consider the Data Science Professional Certificate from IBM, where you'll develop the skills, tools, and portfolio to have a competitive edge in the job market as an entry-level data scientist.


professional certificate

IBM Data Science

Kickstart your career in data science & ML. Build data science skills, learn Python & SQL, analyze & visualize data, build machine learning models. No degree or prior experience required.


(61,917 ratings)

173,169 already enrolled


Average time: 5 month(s)

Learn at your own pace

Skills you'll build:

Data Science, Deep Learning, Machine Learning, Big Data, Data Mining, Github, Python Programming, Jupyter notebooks, Rstudio, Methodology, CRISP-DM, Data Analysis, Pandas, Numpy, Cloud Databases, Relational Database Management System (RDBMS), SQL, Predictive Modelling, Data Visualization (DataViz), Model Selection, Dashboards and Charts, dash, Matplotlib, SciPy and scikit-learn, regression, classification, Hierarchical Clustering, Jupyter Notebook, Data Science Methodology, K-Means Clustering

Written by Coursera • Updated on

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

Develop career skills and credentials to stand out

  • Build in demand career skills with experts from leading companies and universities
  • Choose from over 8000 courses, hands-on projects, and certificate programs
  • Learn on your terms with flexible schedules and on-demand courses