Google·Data Scientist·Technical Phone Screen
May 2026
Google DS interview, one big causal inference question that took up basically the whole session. The depth they expected was pretty serious, covering everything from identification assumptions to sensitivity analysis. Felt like a take-home problem crammed into a live conversation.
- You have observational data on a recommender feature where users self-selected into treatment. Walk through how you'd use propensity score matching to estimate the causal effect on 7-day retention, covering identification assumptions, covariate selection, matching mechanics, balance checks, ATE vs ATT, uncertainty quantification, and what you'd do if PSM falls short.
“This was a lot to hold in your head at once.” The rest of the author's notes on Data Scientist interview at Google, Technical Phone Screen round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.
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