Google·Data Scientist·Technical Phone Screen
Jun 2026
A Google Data Scientist interview that was essentially one massive case question about Google Meet call drops. The whole thing was a structured product analytics exercise but it went pretty deep into experimentation, business impact quantification, and prioritization frameworks all rolled into one.
- A major enterprise customer is reporting frequent Google Meet call drops. Walk through a complete analysis plan: how would you define a 'drop,' what instrumentation would you need, and how would you handle correlated failures to avoid double-counting?
- How would you quantify the business impact of call drops across three layers: meeting time lost, user productivity, and account-level retention risk? And how would you estimate the marginal impact of a 1 percentage point increase in the drop rate?
- How do you decide whether to prioritize fixing this bug versus building a new feature? Walk through the expected value calculation and how you'd factor in opportunity cost and guardrail metrics.
- How would you determine whether this increase in call drops is a regression? What signals or experiment designs would you use?
- How would you structure a decision memo for this situation, including your assumptions, a sensitivity analysis, and the specific observations that would invalidate your recommendation?
“The definition piece tripped me up more than I expected.” 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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