Google data scientist interview preparation
These are independent data science practice exercises, not confirmed Google questions. Google’s Careers entry point links interview preparation, but role-specific instructions should come from your recruiter. Do not assume a software-engineering process applies to a data science role.
Before you practice
Read the job description. Identify the responsibilities you would own. Ask about the assessment format, time limit, data, allowed tools and AI-assistance policy. Practice with public or synthetic data, and be ready to explain a step without relying on an assistant’s output.
How would you design an experiment?
Practice scenario: A product team wants to know whether a new feature changes completion. Explain the outcome, allocation and analysis questions.
What to explain: Define eligibility, unit of assignment and outcome before observing results. Consider interference, instrumentation, sample coverage and uncertainty. Distinguish the chosen decision metric from exploratory slices and explain what would invalidate the comparison.
How would you evaluate a prediction model?
Practice scenario: A high notebook score falls after deployment. Outline plausible causes and checks.
What to explain: Check leakage, preprocessing consistency, training versus serving inputs and changing populations. Compare a suitable baseline and inspect error costs. Keep the final evaluation independent of model selection; a single headline score does not establish usefulness.
How would you explain an inconclusive result?
Practice scenario: The team wants a binary recommendation from limited evidence. Explain how you would respond.
What to explain: State what the data supports, what remains uncertain and which decision assumptions matter. Propose a practical next check and distinguish a business choice under uncertainty from a statistically established effect. Avoid inventing confidence to satisfy the request.
Review your answer
Check whether you defined the question, justified the method and proposed a way to verify the result. Include a failure case and a limitation. Our suggested rubric assesses reasoning and evidence; it is not an employer’s official scoring system.
Questions for the interviewer
- Which deliverable would I own, and who reviews it?
- What data-quality or operational problems does the team currently face?
- How do you assess success and support learning in this role?
Sources and next steps
Sources checked on October 2, 2026. Follow the linked documentation for current requirements.