Here is How to Structure your Data Science Resume (Examples for Every Background)
Learn exactly how to structure your resume depending on your background
This is the most common resume advice you’ll hear get thrown around:
“Make it ATS-friendly”
“Quantify your impact.”
Great, and then what?
Because no matter how good the advice is, there’s no one-size-fits-all. Your resume needs to tell the right story for your background.
A career switcher shouldn’t structure their resume like a recent grad. And experienced data scientists with multiple roles? Totally different game.
This isn’t just formatting, it’s psychology. You’ve got seconds to convince someone (most likely a non-technical recruiter) that you’re worth reading. So you have to get it right the first time!
This is why, in this article, I’ll break down exactly how to structure a winning data science resume, based on who you are.
This is all based on my experienced as a career coach and ex-hiring manager.
🎁 At the end, I’ll also share my go-to ATS-friendly resume template.
Let’s dive in.
Core principles of a standout resume
First, let’s review the basics, because even small mistakes here can ruin a great resume.
1. One-page vs. two-page
Unless you have 7+ years of experience and multiple relevant roles, stick to one page.
Two-pagers aren’t automatically bad, but you need a clear reason for it (we’ll talk more about this in the next section). You don’t want to make the recruiter or hiring manager work harder than they have to qualify you.
2. Bullet structure that works
Every bullet should follow this simple formula:
Action verb + What you did (how/tool) + Outcome (quantified if possible)
Example: Built a recommendation system using XGBoost, increasing user engagement by 18%.
💡 I should mention that leading with outcome can also be very effective, but only if the outcome is impressive and clear without needing context. Otherwise, defaulting to: Action verb + What you did (how/tool) + Outcome is safer and easier to follow, especially for hiring managers skimming fast.
If you need inspiration on how to quantify your impact, checkout this paid article.
3. Keywords vs. clarity
One of the biggest misconceptions I see is thinking that the skills section is where you prove you're qualified.
It’s not.
The skills section is just a quick scan for familiarity, it’s a checklist. But both the ATS and the human reading your resume care much more about where those keywords show up.
For example, if you list “Python, SQL, XGBoost” under skills, that’s great, but it won’t carry much weight unless those same terms show up in your actual experience bullets.
Why? Because that’s where context lives.
It shows how you used the tool, what kind of problems you solved with it, and what kind of results you achieved. That’s what helps both the ATS rank you higher and the recruiter understand how you’ve applied your skills in practice.
💡 Keywords in the skills section help you get found. Keywords in your experience section help you get chosen.
4. Layout & formatting tips
Design shouldn’t distract. The goal is to make your resume effortless to read, for both the ATS and a human skimming it in under 20 seconds.
Here’s what that actually looks like:
Use a single-column layout: Multi-column designs confuse ATS systems.
No tables or graphics: They might look nice, but they often break when parsed by ATS. Keep it simple.
Prioritize whitespace: This isn’t just about aesthetics, it’s about making your content breathable and skimmable. Use spacing between sections and bullets intentionally.
Consistent formatting: Font size, alignment, bullet spacing, it all adds up. If your layout is inconsistent, it feels sloppy, even if the content is great.
Alright, now that we got the basics out the way, let’s go over the structure…
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