Google Data Scientist candidates report interviews emphasizing SQL, experimentation, product metrics, modeling judgment, and behavioral communication, with formats varying by team and level.
$199K
Avg. Base Comp
$319K
Avg. Total Comp
3-5 rounds
Typical Rounds
4-8 weeks
Process Length
Google Data Scientist interviews reported here repeatedly test how you reason from a product question to an analysis or decision. Experiment design and metric tradeoffs are recurring themes. Candidates describe cases involving feature launches, ranking changes, Maps, and other product scenarios: define success, choose metrics, design an experiment, and explain what you would do when results conflict or an experiment cannot be run.
SQL is commonly paired with that judgment. Reported exercises include joins, rolling active-user calculations, retention, and product questions using event data. Candidates also describe data-cleaning issues such as duplicate user IDs and missing timestamps. Practice narrating the logic, definitions, assumptions, and edge cases rather than treating the query as an isolated syntax task.
Modeling discussions are generally applied rather than purely theoretical. Candidates report clustering, engagement modeling, recommendation design, model selection, evaluation, cold start, and drift. Statistics discussions include probability, simulation, sampling, experiment bias, sample size, seasonality, and conflicting metrics. Behavioral conversations may ask candidates to explain projects, leadership, collaboration, and difficult decisions.
Reported formats differ: some candidates had three onsite interviews after initial contact, while another described two screening rounds, two technical rounds, and a final Googliness conversation. Other reports describe technical screens followed by an onsite that revisited similar formats. Prepare for the recurring topic areas, but confirm the sequence with your recruiter.
Synthesized from 35 candidate reports by our editorial team.
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Real interview reports from people who went through the Google process.
The only question I was asked that stood out was behavioral: I was asked to describe a time I worked with data. I applied in June and made it through to the final interview, although I was still waiting to hear back after finishing the process. The experience itself felt positive, and my impression was that the culture was strong and there were a lot of opportunities at Google. My advice is to prepare a clear, concrete example of a data project you have worked on, including your role and how you approached it. I ultimately did not receive an offer.
Prep tip from this candidate
Prepare a specific story about a time you worked with data, with enough detail to explain your role and approach.
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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 | |
| Bucket Test Scores | |
| Complete Addresses | |
| Daily Retention Summary | |
| RMS Error | |
| Find Bigrams | |
| Delivery Estimate Model | |
| Reducing Error Margin | |
| Random Bucketing |
Synthesized from candidate reports. Individual experiences may vary.
Candidates sometimes report a recruiter conversation covering experience, role logistics, or a brief technical question, while others say they moved directly to technical interviews. Use this stage to confirm the planned format and preparation expectations.
Candidates report SQL exercises involving joins, rolling activity, retention, and event data, often alongside an applied product prompt. Interviewers may ask for definitions, assumptions, and edge cases as you explain your query.
Candidates report A/B-test and measurement cases covering success metrics, randomization, sample size, bias, novelty effects, p-values, and conflicting metrics. Some reports describe a single scenario explored through layered follow-up questions.
Candidates report open-ended cases on product launches or features that move from goals and metrics into modeling. Discussions may cover data needs, clustering or recommendation approaches, evaluation, cold start, and model behavior over time.
Candidates report behavioral, leadership, presentation, and Googliness-style conversations in some processes. Be ready to explain projects clearly, discuss collaboration or disagreement, and defend your reasoning to different stakeholders.
