Nasdaq Data Scientist interview typically runs 4 rounds: HR screening, hiring manager interview, take-home data project, final round. The process usually takes about two weeks and is notably compressed, with the take-home carrying heavy weight.
$130K
Avg. Base Comp
$158K
Avg. Total Comp
3
Typical Rounds
2-4 weeks
Process Length
We’ve seen Nasdaq care less about a wide survey of data science topics and more about whether a candidate can go deep on one piece of work and defend the choices behind it. In the candidate experience we reviewed, the hiring manager spent most of the conversation drilling into a master’s capstone, especially the NLP component, which suggests they’re looking for clear ownership and technical judgment rather than polished generalities. That’s a useful signal for anyone interviewing here: if your background includes a project that maps to the team’s domain, expect it to become the center of gravity.
A recurring theme is how heavily the company appears to lean on the take-home project as a decision point. The candidate described it as a major part of the process and noted that it normally takes about two weeks, which makes the compressed delivery here stand out even more. That tells us Nasdaq is likely evaluating not just the final output, but also how candidates structure ambiguous work under real constraints. The non-obvious make-or-break factor is how well you can explain your tradeoffs, assumptions, and methodology when the project is under scrutiny. Our candidates report that the process can feel stressful when timing slips, so the strongest signal is often not perfection, but whether your work still reads as deliberate and defensible when the clock gets tight.
Synthetized from 1 candidates reports by our editorial team.
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Real interview reports from people who went through the Nasdaq process.
The part that stood out most was how much weight they put on a take-home data project. I later heard it normally takes about two weeks to complete, but I didn’t even receive it until the day before the final round because of an email security issue on their side. That meant I had to drop everything and rush to get as much done as possible before the interview, then finish and send the completed project by Friday. The whole thing felt pretty compressed and stressful, especially since that project was clearly a key piece of the process.
Before that, I had an initial HR screening that was pretty standard and mostly covered resume basics. After that I spoke with the hiring manager, and that round was much more focused than I expected. He spent a lot of time digging into my master’s capstone project and not much else, with a strong interest in the NLP side of it. It felt like they really wanted to understand the depth of that project and how I approached it, rather than testing me on a broad set of data science topics. I ended up not getting the offer, and the main takeaway for me was to be ready to discuss one project in real detail, especially if it matches the team’s area of interest, and to leave enough time for a take-home if that’s part of the process.
Prep tip from this candidate
Be ready to go deep on one project, especially an NLP capstone if that’s the area they care about, and expect the take-home project to be a major part of the final discussion. If they send a project, don’t assume it’s lightweight — it may be something they expect you to spend days on, not hours.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Nasdaq
Write a query to return whether each user's subscription date range overlaps with any other completed subscription
| Question | |
|---|---|
| Prime to N | |
| Find the Missing Number | |
| Bank Fraud Model | |
| Rectangle Overlap | |
| Hurdles In Data Projects | |
| String Subsequence | |
| Google Maps Improvement | |
| Nearest Common Ancestor | |
| Fair Coin | |
| Groups of Anagrams | |
| Longest Increasing Subsequence | |
| Binary Tree Validation | |
| Find Duplicate Numbers in a List | |
| Target Indices | |
| Dijkstra implementation | |
| Filling Supermarket Bag | |
| Median O(1) | |
| Assumptions of Linear Regression | |
| 5th Largest Number | |
| Target Value Search | |
| Implementing the Fibonacci Sequence in Three Different Methods | |
| Radix Addition | |
| Concurrent LLM Serving | |
| Most Repetition | |
| Finding the Maximum Number in a List | |
| Moving Window | |
| String Palindromes | |
| Confidence Interval Explanation | |
| NxN Grid Traversal |
Synthesized from candidate reports. Individual experiences may vary.
A standard initial screening with HR focused on resume basics and general background. This stage appears to be a straightforward check of fit and experience before moving to more substantive interviews.
A focused conversation with the hiring manager that dug deeply into the candidate’s master’s capstone project, especially the NLP components. The discussion emphasized understanding the project in detail, the candidate’s approach, and how closely the work aligned with the team’s interests.
A key part of the process was a take-home data project that was reportedly expected to take around two weeks. In this case, the assignment was delivered very late due to an email security issue, making the timeline compressed and stressful; the completed work was due by Friday before the final round.