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Airbnb data scientist interview (SQL + case + stats): what each round tests

ds_dmitri (Primly starter) · 5 replies

Went through the Airbnb DS interview loop in February 2026 for a mid-level DS role on the trust and safety side. The loop is three technical rounds plus a behavioral, and each one tests something distinct. Breaking it down.

Round 1: SQL This is a real SQL round, not a warmup. You'll write queries in a shared environment. My questions: Window functions: rank guests by booking value within a region over the last 90 days A multi-table join scenario involving hosts, bookings, and listing attributes One question that required a self-join I did not see coming

Biggest tip: write it out step by step, explain what you're doing. They grade the thinking as much as the final query. Don't just silently type.

Round 2: Product/case analytics You're given a business scenario and need to diagnose and recommend. Mine was: host acceptance rates dropped 8% in one region over 4 weeks. What do you investigate, what metrics do you pull, what do you recommend?

This is where knowing Airbnb's product deeply pays off. The better your intuition about supply-demand dynamics, host psychology, and marketplace mechanics, the more credible your diagnosis sounds. Generic "check for seasonality, check for bugs" is fine but doesn't impress.

Round 3: Stats / experimental design This is the round people underprepare for. Topics that came up in mine: Power calculation for an A/B test on a low-frequency event (they pushed on how you'd handle low sample size) Difference-in-differences setup for a policy change that couldn't be A/B tested Bayesian vs. frequentist framing question, conversational, not a proof

I'd say the stats round is harder than the SQL round if you haven't done much causal inference or experimental design in your day job.

Behavioral: Standard Airbnb values-based questions, same as described elsewhere on this forum. Have impact stories ready that involve influencing stakeholders on data findings, not just individual analysis work.

Offer: I got an L4 equivalent offer (Airbnb doesn't always use L numbers for DS). Total comp was around $210k for a Seattle-remote arrangement. I was expecting SF-anchored, so the number came in slightly under what I targeted but still solid.

5 replies

analyst_ana (Primly starter)

The self-join question is the kind of thing that trips people up when they've only practiced the standard interview SQL. Good to know it's in scope.

numbers_only (Primly starter)

$210k Seattle-remote L4 DS. Is that base + RSU annualized, or base only? Useful data point if you can break it down.

ds_dmitri (Primly starter)

Total comp: ~$155k base, ~$50k RSU annualized (4-year vest, standard cliff), ~$8k bonus target. No signing in the written offer but I didn't push on it, probably should have.

de_derek (Primly starter)

Diff-in-diff in a DS interview is interesting. That's edging toward data science research rather than applied analytics. Is the trust/safety team more research-oriented than typical Airbnb DS?

Primly Team

One stage people tend to underestimate in Airbnb loops is the values-focused conversation. Airbnb is known for weighting values and hospitality heavily in interviews, and in other functions it can outweigh a strong technical showing. For DS, that usually shows up as: can you communicate with empathy, make tradeoffs that protect users, and work cross-functionally without defaulting to “just run an experiment.”

A practical way to prep is to pre-write 3 to 4 stories that map to: (1) handling sensitive or ambiguous problems, (2) partnering through disagreement, (3) improving an experience for a specific group of users or stakeholders, and (4) learning from a miss. Then, for each story, add a “principle” sentence and a “how I’d do it differently next time” sentence. Interviewers often probe those.

What values or culture questions did you get in DS interviews, and what kinds of stories seemed to land best?