Candidates describe the Google Data Scientist interview as a mix of SQL with window functions, A/B testing and statistics, product and ML cases, and a Googleyness behavioral round. Formats vary by team and level.
$177K
Avg. Base Comp
$320K
Avg. Total Comp
4-5 rounds
Typical Rounds
Not reported
Process Length
Across the reports for Google's Data Scientist interview, the most consistent theme is experimentation and statistics. Several candidates describe designing an A/B test end to end: choosing the success metric, estimating sample size, handling novelty effects and seasonality, and deciding what to do when metrics conflict, such as click-through rising while session time falls. Others report sample ratio mismatch checks, randomization-unit validation and questions on p-values, sampling and the central limit theorem.
SQL appears often, and candidates mention window functions in several reports. Examples include a 7-day rolling average of daily active users, retention metrics, and the time to a user's third transaction. Candidates also say the interviewer wanted to hear the reasoning about definitions and edge cases, not just the query. One report describes writing SQL in a Google Doc-style setup instead of running it.
Product and ML cases tie to Google products. Candidates mention improving or measuring Google Maps features, a YouTube homepage recommender and a YouTube Shorts engagement model. Interviewers reportedly push back on assumptions, so expect to defend metric and model choices out loud. Python is described as light list processing or data cleaning in some reports and as ML-related coding in others. One candidate was asked to design a spam classifier and write bi-gram pseudocode.
Most candidates mention a Googleyness or behavioral round about collaboration, disagreement and leadership. Loops differ by level and team, and outcomes range from offers to rejections, so treat each report as one candidate's loop.
Synthesized from 40 candidate reports by our editorial team.
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| Question | |
|---|---|
| 2nd Highest Salary | |
| Top Three Salaries | |
| First Touch Attribution | |
| First to Six | |
| Merge Sorted Lists | |
| Experiment Validity | |
| String Shift | |
| 500 Cards | |
| Last Transaction | |
| Button AB Test | |
| Top 3 Users | |
| Raining in Seattle | |
| Third Purchase | |
| Job Recommendation | |
| Minimum Change | |
| Impression Reach | |
| Jars and Coins | |
| Lazy Raters | |
| WAU vs Open Rates | |
| Find the First Non-Repeating Character in a String | |
| Network Experiment Design | |
| Daily Retention Summary | |
| Bucket Test Scores | |
| Complete Addresses | |
| RMS Error | |
| Find the Index with Equal Left and Right Sum | |
| Find Bigrams | |
| Reducing Error Margin | |
| Delivery Estimate Model |
Synthesized from candidate reports. Individual experiences may vary.
Several candidates report a short recruiter or HR call covering their background, role expectations and logistics. One candidate says it included a Bayesian statistics question, and another says they were emailed straight to a technical screen with no HR call. Formats may vary by team and level.
Candidates report one or two technical screens. Reported content includes SQL with window functions, light Python, probability and statistics, A/B testing and an ML case. One junior candidate describes a scenario-only round with no coding. Expect to explain reasoning aloud, and note that the recruiter's description may not match the interviewer's actual format.
Candidates commonly describe designing an experiment for a feature or thumbnail change. Reported follow-ups cover metric choice, guardrails, sample size, power, novelty effects, seasonality, sample ratio mismatch and conflicting results. Interviewers may keep layering questions onto a single scenario.
Reports describe product cases such as measuring a Google Maps feature, debugging a 10% engagement drop, or framing a YouTube recommendation or engagement model. Candidates say interviewers probe assumptions on data availability, cold start, drift and evaluation, and want a clear problem framing before modeling.
Candidates report a behavioral round near the end of the loop, often called Googleyness. Reported topics include leadership, difficult decisions, disagreeing with a PM and handling pushback on data. Candidates suggest preparing STAR-style stories with measurable impact. Outcomes after the loop reportedly vary widely.
