Two candidates describe the Nasdaq Data Scientist interview: an HR screen, a hiring manager conversation, a take-home or case-study assessment, and in one account a final round with product and leadership.
$120K
Avg. Base Comp
$148K
Avg. Total Comp
3-4 rounds
Typical Rounds
Not reported
Process Length
Two candidates describe the Nasdaq Data Scientist interview, and both emphasize applied work over broad quiz-style testing. Both began with an HR screen and then met the hiring manager, so expect to discuss your background early. Their accounts differ after that, so treat them as two separate experiences.
One candidate says the hiring manager spent most of the conversation on their master's capstone, particularly its NLP component. They describe a take-home data project as a major part of the process. They also heard it normally takes about two weeks, but they received it only the day before the final round and had to finish it by Friday. If you get an assignment, ask early about the timeline and keep time free.
The other candidate chose one of two offered case studies and presented it to a data scientist and a statistician. Follow-up questions moved into machine learning fundamentals and some LLM concepts. This candidate's final round was with a product owner and a VP, and it focused on product thinking and explaining technical ideas to non-technical stakeholders. One question asked them to explain machine learning versus deep learning to someone with no technical background.
Both candidates reported no offer. One said the promised follow-up questions never arrived and that they got no response afterward. That is one account, but it suggests you should confirm next steps and timing at each stage. To prepare, pick one project you can explain in real depth, rehearse a case-study presentation, refresh ML and LLM basics, and practice plain-language explanations.
Synthesized from 2 candidate 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.
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Synthesized from candidate reports. Individual experiences may vary.
Both candidates report opening with a short HR conversation. One describes it as standard and mostly about resume basics. The other says it covered their background and an overview of the role. Expect to summarize your experience and your interest in the position.
Both candidates met the hiring manager next. One says the conversation focused almost entirely on their master's capstone, especially its NLP side. The other describes a more motivational discussion about communication and collaboration. Be ready to go deep on one project and to explain how you work with others.
One candidate received a take-home data project, which they heard normally takes about two weeks, and had to rush it before the final round. Another chose one of two case studies and presented it to a data scientist and a statistician. Follow-up questions covered ML fundamentals and some LLM concepts. Clarify the format and deadline early.
One candidate reports a final round with a product owner and a VP who is also a solution architect. It centered on broader experience, product thinking, and translating technical ideas for non-technical stakeholders. The other account mentions a final round but does not describe its content. Both candidates reported no offer, and one received no follow-up afterward.
