Why Your Data Science Resume Isn’t Landing Interviews (and How to Fix It)
Is the ATS holding you back?
It's unfortunate, but many of the data science candidates I've coached open up with:
Help, the ATS keeps ignoring my resume!
But here is the thing...
Although the ATS can in fact hurt your chances of getting noticed.
More often than not, the main issue lies elsewhere, and focusing on the wrong problem will only distract you from addressing what’s truly holding you back from landing interviews.
So in this article, I’ll help you diagnose what could be going wrong with your resume and give you the strategies to fix it.
We’ll cover the following:
How the ATS (actually) works
The best strategies to beat the ATS.
5 common reasons you are not landing interviews
🎁 Plus at the end, for all my paid subscribers, I will share these two great resources:
Free ATS-friendly resume template
Free ChatGPT prompt for beating the ATS
Let’s get started!
How the ATS (actually) works
There is too much misinformation out there about what Applicant Tracking Systems (ATS) are and how they work, so let’s start by understanding what we are really up against.
First, ATSs are designed to streamline the hiring process by scanning resumes for specific keywords, formatting, and qualifications.
You can think of it as a filter: if your resume doesn't align with the job description or uses unconventional layouts, it risks being deprioritized, or worse, ignored altogether.
Let’s use Teamtailor as an example (since this is the one I used as a hiring manager):
Their system includes a Job Match Score that evaluates how well a candidate’s resume aligns with the role’s requirements, meaning recruiters and hiring managers can quickly identify top matches for the job.
💡 So if your resume doesn’t clearly highlight the relevant skills or keywords, your score drops, making it less likely you’ll stand out in a crowded applicant pool.
And just to be clear, this isn’t just how Teamtailor works, it is basically how almost all ATSs out there handle applications (e.g. Greenhouse, Jobvite, Lever, etc…)
Best strategies to beat the ATS
Now that we know the ATS plays a crucial role in our application process and that the better it matches you to a job, the greater your chances of landing in front of a recruiter.
Let’s go over how to prevent it from working against us, and believe it or not, it’s not that complicated.
Two things you should always be doing:
Use keywords strategically: Mirror keywords from the job description exactly. If the job mentions "machine learning pipelines," don’t generalize it as "data pipelines."
Keep your format simple: Avoid graphics, tables, and unusual fonts. Use standard headings like "Work Experience" to ensure the ATS can parse your resume.
It really is that simple!
5 common reasons (beyond the ATS) why you are not landing interviews
Let’s assume your resume is perfectly formatted and optimized for the ATS, but you are still not landing interviews.
What else could be going wrong?
I’ve put together 5 common reasons I’ve seen among the 60+ aspiring data scientists I’ve coached over the past couple of months:
1. Forgetting that a human will also look at your resume
Yes, ATS filters matter, but they aren’t the final decision-makers—people are.
If your resume is crammed, poorly organized, or hard to skim, recruiters won’t bother. Focus on clarity: use headings, bullet points, and concise descriptions to make their job easier.
The easier it is to read, the faster they’ll see your value (and you only have between 15-30 seconds to convince them you are worth an interview).
2. Overpacking your resume with keywords
Keyword stuffing is a trap.
While ATS likes seeing relevant terms, hiring managers don’t want to read a resume that looks like a buzzword checklist.
Instead, highlight where you’ve actually used those skills, paired with real achievements.
Show, don’t tell!
3. Failing to quantify achievements
If you truly want to stand out, vague and generic descriptions won’t cut it.
Saying “improved a process” or “led a project” doesn’t tell anyone why you’re worth hiring. Instead, tie your work to results.
For example, “Improved a recommendation engine, increasing engagement by 20%.”
Quantifiable results make your contributions clear and memorable. You can find more examples in this article 👇
4. Applying for the wrong jobs
If you’re aiming for roles that are too far from your skill set or experience, you’re setting yourself up for frustration.
That doesn’t mean you can’t stretch, but there’s a difference between ambition and misalignment.
Look for roles where you match at least 70% of the requirements, and make sure your resume reflects why you’re a fit. Otherwise, don’t bother applying and place your time and energy elsewhere.
5. Overlooking the state of the market
Sometimes, it’s not about you—it’s about timing.
The job market fluctuates, and over the past year or so we have seen layoffs and hiring freezes while aspiring data scientists fresh out of school are flooding the market, so even a great resume might not make it in these market conditions. Timing and persistence matter here.
Stay proactive and consistent.
📣 What perks do you get as a paid subscriber?
Full access to all public + premium posts.
A free copy of my Data Science Project Ideas PDF (2025 edition).
A free copy of “Data Science Interview Case Studies (2025 Edition)“.
A 25% off coupon for a 1:1 mentoring session.
Occasional live Q&As and exclusive workshops.
🎁 Your free resources
As promised, here are two resources that together will not only help you optimize your resume to beat the ATS but also create a simple but effective resume that is easily skimmable (and believe me, recruiters/hiring managers will love you for that).