Google Data Scientist candidates report SQL with window functions, A/B test design, statistics, product-metrics cases, some ML, and a Googleyness round. Formats vary by team and level, so prepare across all of these.
$196K
Avg. Base Comp
$310K
Avg. Total Comp
4-5 rounds
Typical Rounds
Not reported
Process Length
Candidates describe the Google Data Scientist interview as less about obscure puzzles and more about defending your reasoning. Several people report SQL that expects window functions, such as a 7-day rolling active-user count or the time to a user's third transaction. Candidates also say interviewers care how you clarify the product question first, for example who counts as active and how to handle late events.
Experimentation and statistics come up repeatedly. Reports include designing an A/B test for a notifications feature, a ranking change or a YouTube thumbnail change. Follow-ups cover success metrics, sample size, novelty effects, seasonality, sample ratio mismatch and conflicting metrics such as click-through rising while session time falls. One candidate was asked what a p-value above 0.05 means. Others describe the statistics round as the hardest and say it went deeper than the product round.
Product-sense cases tend to use Google products. Candidates mention improving or measuring a feature in Google Maps, and investigating a usage drop. Modeling questions vary: some candidates describe a high-level ML case or a recommendation design, while others report logistic regression assumptions, churn prediction, or a spam classifier. Several candidates report a Googleyness or leadership round, and one flagged it as influential for leveling.
Formats are not uniform. One junior candidate had a scenario-only round with no coding, and a more senior candidate described two screens, technical rounds and a Googleyness round. Ask your recruiter what each round covers, and practice explaining every metric and method choice out loud.
Synthesized from 40 candidate reports by our editorial team.
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Real interview reports from people who went through the Google process.
The whole process took about three weeks, which felt manageable. It started with a standard HR phone screen where they checked salary expectations and my background — pretty straightforward. Then came a technical video call focused on Python and SQL, which I felt okay about.
The tricky part was a 45-minute data structures and algorithms problem during one of the technical rounds. It was a graph problem, solidly in the LeetCode Medium to Hard range. What caught me off guard was how little guidance the interviewer gave. I had to work through the approach, implementation, and debugging mostly on my own, which added to the pressure. There wasn't much room to bounce ideas off them or course-correct if I was heading in the wrong direction.
The final round was a virtual onsite with the hiring manager and two team members. That one was more conceptual — we talked through past projects, machine learning fundamentals, and cultural fit. One question that stuck with me was about precision versus recall in a fraud detection context, which forced me to think concretely about the trade-offs rather than just regurgitate definitions. It was a good question, honestly, because it made me justify my reasoning.
Overall, it felt like a solid interview process, but I didn't make it through. The algorithm round and final round combination was demanding, and I stumbled on execution.
Prep tip from this candidate
Practice graph algorithms thoroughly and expect to solve Medium-Hard LeetCode problems with minimal interviewer guidance — you'll need to talk through your approach clearly and debug independently. Also prepare concrete examples for ML trade-off questions like precision vs. recall in specific business contexts (e.g., fraud detection), not just textbook definitions.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Google
Write a query that returns all neighborhoods that have 0 users.
| 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.
Candidates typically report a recruiter call covering experience, role expectations and logistics. One candidate says they skipped this call and were invited straight to a technical screen. Another found the recruiter's description of the first round did not match the format, so it may help to ask what each round covers.
Candidates report SQL questions that rely on window functions, such as rolling daily active users, retention, and time to a third transaction. Some also describe basic Python list processing or data cleaning. Reports say interviewers value narrating your logic and edge cases, and some sessions use a Google Docs-style setup rather than running code.
Candidates report designing an A/B test end to end, covering metric choice, sample size, novelty effects, sample ratio mismatch and conflicting metrics. Statistics questions mentioned include p-values, sampling, the central limit theorem and Bayes' theorem. Some candidates describe a scenario-based round with layered follow-ups and no coding.
Candidates describe product cases on Google products such as Maps and YouTube. These include defining success metrics, diagnosing an engagement drop, and sketching a model or recommendation system. Reports say interviewers push back on assumptions, so be ready to defend your choices. Modeling depth varies, from a high-level lifecycle to logistic regression assumptions.
Candidates report a Googleyness or leadership round covering collaboration, disagreements and difficult decisions. Several describe an onsite that repeats the screen formats with more depth, and one adds a project presentation with frequent questions. Candidates may also face hiring committee review, and one reports being downleveled.
