9 Tips for Creating Your Data Science Resume as an Undergrad

Jake Haines
4 min readApr 6, 2021

Knowing where to begin with your resume, especially when you have no prior professional experience and are still an undergraduate student, can be daunting. You need to land that internship this summer, yet you have no idea where to begin. You’re applying to internships and never hearing back, and you suspect it’s your resume’s fault.

Fear not, I was in the same boat recently. I had been doing a spray and pray on job boards with internship applications for months, hardly hearing back from anyone applied to, until I landed an “get-to-know-you-better” interview at a startup called Naivya. I didn’t end up proceeding with the job, as the skill assessment task was a little out of my skillset — but I didn’t walk out of that experience without anything useful. One of the founders, who interviewed me, provided in-depth criticism on my resume and overall professional approach. I took mental note of as many of the tips as I could, and marked up my resume with all the feedback.

Here’s what my old resume looked like:

If you’re looking for style references, make a resume that matches your business card or check out inspirations. Changing the Title/header/subtitle/etc formatting in Word is quite simple and makes customizing one of Word’s resume templates much easier. I’ve omitted the top portion of my resume for privacy reasons.

It’s two pages, made up of a majority of what I was told is essentially BS. Here’s the general notes I had on the resume above:

  • The resume is two pages long. It shouldn’t be two pages long when fresh out of college
  • No one’s going to read the first paragraph
  • Nothing in the list of skills is quantifiable or specific
  • Progress bars are not accurate and should be omitted
  • Any experience without relevant skills should be removed, even if you think work ethic from the job is important. It won’t look relevant to recruiters
  • Create a separate section for research at my university
  • Include skills listed under “software” in the job descriptions. If a skill wasn’t used in your experience, there’s no way to prove proficiency in it
  • If you don’t speak a language at a biliterate level, it shouldn’t be on your resume. You need working proficiency in that language
  • Remove the periods after each description point in job experience.

In my case, I marked up my resume using my Remarkable tablet. You are welcome to print and write on it, or whatever method you can use that allows you to annotate your resume. But if you can get direct feedback on your resume from an industry professional (not just a recruiter), take notes. It will benefit you later.

When I redesigned my resume, it looked much better and I was certainly more confident about it. I even formatted it to match my business cards. Take a look at the resume after revising using the feedback I got from just one interview.

It is much more concise, cleaner, and less pretentious than the previous. With all that in mind, I’ve wrapped up this article in a few points for quick reference:

9 tips for creating a better data science resume

  1. Keep your resume concise and nothing more than a page. Styling and formatting is important when you’re creating your resume, but no need to make anything beautiful. You’re a data scientist, not a freelance graphic designer.
  2. Objectives are optional — I rewrote mine to more accurately reflect my character. If you are sending a cover letter or are emailing someone directly, there’s no need to include an objective.
  3. Do not use progress bars.
  4. Major academic projects (such as undergraduate research) deserve their own experience block.
  5. List skills in comma-delimited fashion. It takes up less space.
  6. Skills should consist of what you have real experience with, not just what you’ve taught yourself on Udemy. If you applied those skills to any project, list it.
  7. Include as many of those skills as possible in your experience blocks.
  8. Only include a language on your resume if you are biliterate in that language.
  9. Only list coursework that is practical to data science

That finishes this article. I hope that this was beneficial to you! If you have any questions, feel free to reach out on LinkedIn or email.



Jake Haines

Data Engineer // Ex-Tesla // Statistics @ NC State Univ.