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Meta data scientist interview (SQL + case + stats): what each round actually looks like

analyst_ana · 5 replies

wrote down my notes the day after my Meta DS loop, sharing them now that I have the outcome (got an offer, L5 DS).

Meta's DS interview is different from most because it's genuinely three separate skill areas and they don't conflate them.

SQL round: this is the most consistent part across every DS loop at Meta that I've heard about. two or three questions, increasing complexity. the first is usually a warm-up: a join, a GROUP BY, maybe a window function. the harder ones involve things like: "find the second highest X per group," or "users who did A but not B within 7 days of signing up." they care about correctness and efficiency. using CTEs is fine and honestly makes your logic clearer to walk through.

one pattern I saw twice: they give you a schema with gaps and ask you to infer what the data means. read the table names carefully, ask clarifying questions.

product case round: this is the metrics/product sense round. they'll say something like "we launched a new feature for Reels, what metrics would you track?" or "engagement on Stories dropped 8%, what's going on." same structure as the PM analytical round but they want you to go deeper on statistical validity: sample size concerns, selection bias, p-hacking risks. I mentioned that a 1-week drop might not have enough data to distinguish signal from noise and that landed well.

stats / experimentation round: this is where Meta separates itself from most other DS interviews. they expect real A/B testing knowledge: how to handle low-power tests, what to do when network effects violate SUTVA, how to handle novelty effects in a recommendation system. I was asked how to design an experiment where users are social and you can't just randomize individually. answer: cluster randomization or using an ego-network holdout.

prep that helped: SQL: Mode Analytics practice problems, plus a mock where I spoke out loud while coding stats: the classic Kohavi et al. trustworthy online controlled experiments book is overkill but worth it case: practice telling a story with a metric drop, not just listing possible causes

5 replies

analyst_ana

the cluster randomization answer for the social network question is exactly right. I was told that answer is a differentiator in that round. they don't expect everyone to know it but those who do get noticed.

ml_mike

what was the seniority of the stats questions at L5? I'm applying L6 and wondering how much further they push on causality / observational methods.

ds_dmitri

at L5 they expected me to know the limits of A/B testing and have one alternative in my toolkit (I used regression discontinuity as an example). at L6 from what I've heard they want you to propose the right method given the constraint, not just know them exists.

returner_ren

how long was the full loop? and was it all remote or a mix?

ds_dmitri

all remote for me. 4 rounds total, done in one day with breaks. about 4 hours active. recruiter screen was a separate call the week before.