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NVIDIA data scientist interview: SQL, case, and stats questions, broken down

analyst_ana · 5 replies

Went through the NVIDIA data scientist interview loop for a role on the cloud analytics team (supporting DGX Cloud adoption metrics). Going to break down the SQL, case, and stats components because that's what I searched for and couldn't find.

Structure of my loop: Recruiter screen (30 min) Technical phone screen with DS manager (45 min: SQL + one stat concept) Virtual onsite: 5 rounds total

Phone screen SQL: One medium SQL question. Window functions. Something like 'find the second-highest revenue customer per region per month.' Window functions and CTEs are table stakes; know them cold. The DS manager also asked me to explain the difference between a correlated subquery and a join in terms of performance. That's the kind of thing that filters people out fast.

Onsite breakdown:

SQL round (60 min): Two SQL problems. First was moderate (rolling averages, standard window function). Second was harder: a recursive CTE problem about hierarchical data. I'd practiced recursive CTEs but not enough. Got through it but not cleanly. Know your recursive CTEs.

Stats / ML round (60 min): Started with probability basics (classic conditional probability question), then moved into experiment design. 'We want to run an A/B test to see if a new onboarding flow increases DGX Cloud trial activation. What do you measure, how do you size the test, what are your power and significance thresholds.' I spent most of the round on this one. They wanted me to walk through sample size calculation out loud. Brush up on power analysis math, not just the concept.

Case / product sense round (60 min): This was the closest thing to a PM interview. 'User activation for DGX Cloud has dropped 15% over the past month. How do you diagnose it.' Standard product analytics framework but they wanted quantitative specifics at each step: what metrics, what queries, what the expected baseline is.

Behavioral + HM (45 min): Standard stories. Focus on data-driven impact. Have at least one story where your analysis changed a product decision and you can give the before/after numbers.

Overall DS difficulty at NVIDIA: harder than mid-market tech, comparable to a rigorous Google or Meta DS loop. They genuinely care about statistics depth, not just SQL fluency. The recursive CTE is probably not universal but know it anyway.

5 replies

analyst_ana

The recursive CTE thing trips up so many people. I've seen it in DS interviews at two companies this year and it's not the kind of thing you just figure out on the fly. Good call flagging it.

de_derek

Correlated subquery vs. join performance question is sneaky. Most DS candidates haven't had to care about this unless they've worked on large tables. The answer is basically 'correlated subquery re-executes for each row, join happens in one pass' but explaining it clearly under pressure is different from knowing it.

content_cole

Yeah, and the follow-up was 'when would you use a correlated subquery anyway.' The answer is almost never at scale, but there are edge cases (certain EXISTS checks). Having a nuanced answer matters.

qa_quinn

Any comp data from your offer or the offer stage? DS comp at NVIDIA is hard to calibrate.

hardware_hugo

My offer was $185K base, around $110K RSU/year at current vest schedule, Santa Clara. They came in around 10% below what I asked on base but the equity made up for it given the stock. Ended up passing for a remote role but NVIDIA was competitive.