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Microsoft data scientist interview (SQL + case + stats), full breakdown of my 2026 loop

analyst_ana · 6 replies

just finished a microsoft data scientist loop, targeting a role in the experimentation/analytics org. 5 rounds over two days (virtual). sharing the full breakdown because the ds interview content for microsoft specifically is thin compared to google/meta/amazon.

round 1: SQL not optional, not easy. they gave me two problems. the first was a join + aggregation question, medium difficulty. the second involved window functions (lag/lead + rank) and required me to compute rolling retention rates. i had about 30 minutes per problem. coderpad environment. no autocomplete.

if you're going into a microsoft DS role assuming sql will be light: don't. the experimentation org cares about whether you can actually query data.

round 2: stats + experimentation this is the one that separates people. questions i got: 'an A/B test shows a significant result but the sample sizes were different across treatment and control. what do you do?' 'how would you handle a metric where you expect a strong network effect to contaminate your control group?' 'what's the difference between type I and type II error in the context of shipping a product feature?'

the network effect / interference question is classic experimentation at scale. they want SUTVA, not just a textbook answer.

round 3: case / product analytics given a scenario: 'bing search sessions have dropped 8% month over month in a specific geography. walk me through how you'd diagnose this.' very standard product analytics case. expected structure: external factors, product changes, data quality issues, segment breakdown. i did all of that plus proposed a specific analysis plan with the metrics i'd pull.

round 4: ML concepts not super deep for this role (experimentation focused). asked about bias-variance tradeoff, how i'd evaluate a recommendation model offline vs. online, and when i'd use a simpler model over a complex one. no coding here.

round 5: behavioral standard growth mindset framing. one question was specifically about how i communicated a data-driven recommendation to a non-technical stakeholder who pushed back.

the whole process was respectful of my time. they were direct about the timeline. offer came 6 days after my final round.

6 replies

analyst_ana

the network effect / interference question scares me. what level of depth did they want on that? like did you need to know the specific academic term (SUTVA) or was a conceptual answer about cluster randomization enough?

ds_dmitri

conceptual was fine but you had to have a concrete solution, not just name the problem. i talked through cluster randomization and also mentioned geo-based holdout as an alternative. i didn't explicitly say 'SUTVA violation' but i described the violation clearly. they pushed me to think through tradeoffs of each approach. i think knowing the terminology helps you get there faster but it's not required.

ml_mike

curious if the ml round covered any causal inference or if it was mostly supervised/unsupervised basics. my experience is experimentation-focused ds roles sometimes go deep on causal vs. predictive distinctions.

ds_dmitri

light causal. they asked me to describe difference-in-differences at a high level and i mentioned DiD and synthetic control as options for quasi-experiments. they seemed pleased but didn't go super deep on the math. the emphasis was clearly on understanding the intuition, not deriving anything.

de_derek

six days to offer is genuinely fast for a big tech company. was that typical in your recruiting cohort or did you get the impression you were competing and they moved quickly?

ds_dmitri

recruiter told me the team had open headcount and was prioritizing filling it. i think that was the main driver. i've heard other microsoft candidates wait 2-3 weeks. so don't read into my timeline too much.