Data Scientist interview
at Google.

Tell-me-about-a-time stories on ownership, conflict, and impact — scored against this company's real values.

1. 01 Tell me about a time you used data to challenge a decision that leadership or a product team had already made.
2. 02 Describe a situation where stakeholders disagreed on which metric should define success, and how you drove alignment.
3. 03 Tell me about a time an analysis you delivered turned out to be wrong or misleading, and what you did once you realized it.
4. 04 Describe a project where you had to operate with ambiguous requirements and limited data to still deliver an actionable recommendation.

The hard screen for the craft itself — talked through out loud, not whiteboarded in your head.

1. 01 Write a SQL query using window functions to find each user's second purchase and the time gap between their first and second purchases.
2. 02 An A/B test on a Search feature shows a statistically significant lift in clicks but flat revenue, so how would you reconcile and explain that?
3. 03 How would you detect and diagnose sample ratio mismatch in a large-scale Google experiment, and what would you do about it?
4. 04 Given a metric that suddenly dropped 8% week-over-week, walk me through how you'd structure a root-cause analysis.

Open-ended design of the systems this company actually runs, with the interviewer probing your tradeoffs.

1. 01 Design the experimentation platform Google would use to run thousands of concurrent overlapping A/B tests across Search and Ads.
2. 02 Design a metrics and logging pipeline to measure daily active users and engagement for YouTube at billions of events per day.
3. 03 Design an end-to-end framework to evaluate whether a new Google Maps routing change actually improves real-world trip times.
4. 04 Design a system to detect and quantify novelty effects in a Google product experiment over its first several weeks.