How to Pass Data Science Interviews in 2025
Why most people don't make it pass the first round
The worst mistake I’ve seen many candidates make is thinking that their resume alone will help them land a data science job.
The reality is that it doesn’t matter how strong of a resume you have, in this market, that’s simply not enough.
I recently coached a data scientist who was the perfect example of this.
She checked all the boxes you can think of to help land interviews:
Extensive education
Relevant work experience
A strong portfolio
Optimized resume
But landing interviews wasn’t her problem, it was getting through the rounds to ultimately land an offer.
So today, I want to help you prepare for data science interviews and share some of the strategies that ultimately helped my mentee land two job offers.
This is what we’ll cover today:
Getting familiar with the data science interview
3 common mistakes you should avoid
Your secret weapon to become a top 1% candidate
The Data Science interview process
If you want to walk into data science interviews with confidence then you have to start by familiarising yourself with the process.
What I’ve found is that the typical process consists of between 4-6 rounds, which often include these 3 elements:
Behavioral and cultural fit questions
Technical deep dives
A take-home assignment
Here is an example of what 6 rounds could look like:
Recruiter call: First contact with your recruiter to get a general introduction to the role.
Hiring manager interview: Meet your potential manager/team lead, learn about the team and tech stack, and ask questions.
Take-home assignment: Demonstrate your technical by completing a take-home assignment.
Technical interview: Test your technical skills on the spot by solving coding and business use case challenges.
Team meeting: Freeform meeting to see who you’d be working with.
Final interview: Meeting with CEO to get the final stamp of approval or final check-in with the hiring manager.
💡 Keep in mind that every company does it a little differently, some may only test you via a grueling technical interview and leave out the take-home assignment altogether. Similarly, mid-size to large companies usually skip the final interview and jump straight into the salary negotiation.
Nevertheless, this should give you a general sense of what to expect.
Needless to say, it can be intense!
3 Common mistakes you must avoid
Stop getting the dreaded “we have decided not to move forward….“
After coaching many aspiring data scientists and interviewing others as a hiring manager, I've seen a few recurring mistakes that often derail even the strongest candidates.
The biggest mistake you can make? Focusing solely on the technical interview and neglecting the behavioral and cultural fit questions.
💡 We know that technical skills are crucial and that the technical part of the interview is arguably the most challenging of the entire process, but it is only part of what companies evaluate. Employers want candidates who can contribute effectively to their teams and align with the company culture.
So if you wish to present yourself as a strong and well-rounded candidate, do the following:
Don’t overlook behavioral questions — especially “Tell me about yourself”. This is often where interviewers start forming their first impression, and you’ll probably be asked it more than once throughout the process. Make sure your pitch is solid, something that clearly ties together your journey, skills, and what makes you a great fit.
Review your experience and skills in depth. Interviewers will dig into what you’ve put on your resume, so be ready to back up each point with specific examples of your impact. Show them how you think and what you’ve achieved.
Research the company’s values, business, and data approach. This lets you tailor your answers to their needs and also ask smart questions that make you stand out as someone genuinely interested in the company.
Your secret weapon to becoming a top 1% candidate
Spoiler alert! It’s not LeetCode
By now, I hope you realize that despite what some people online might want to make you believe, the data science interview requires much more than just having 50 successfully answered SQL leet code questions under your belt.
The truth is that practicing alone on your computer rarely translates to answering questions confidently and clearly in a high-pressure, live interview setting.
The best strategy to significantly increase your performance and double your chances of acing the data interview and getting an offer is doing mock interviews.
This is what enabled my mentee to confidently and effectively get through her interview rounds and land two job offers.
💡 My friend Mandy Liu is another great example of this. She did 11 mock interviews in preparation for her interview at Meta which eventually landed her a role as a Data Scientist. She knew that she had to up her game if she wanted to get into FAANG, so she left no stones unturned.
But of course, you don’t have to do 11 mock interviews like Mandy did. For most recent graduates and juniors I recommend aiming for 2-3 mock interviews with a clear difficulty progression during the weeks before your real interviews.
You can do this with an experienced data scientist friend or better yet, a data science coach who can create personalized sessions based on your specific needs.
Struggling to land a job?
In the last two months, I’ve helped 4 of my mentees go from rejections to job offers.
If you’re ready to break through, I offer resume reviews, mock interviews, and long-term coaching to help you stand out and land a data job faster.
🎯 Check out my services or reach out to me via email at hello@andresvourakis.com to see how I can coach you.
If you are a paid subscriber, remember that you get 25% off any of my 1:1 coaching sessions.
Thank you for reading! I hope this helps you get ready to ace your next interview.
See you next week!
- Andres
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Now I've counted, I think I actually did more than 11 mocks......(feeling embarrassed)
Amazing tips and examples, i'm Just starting a Data Science bootcamp and eventually will need this information.