Data Scientist Interview Guide 2025

SQL, statistics, machine learning case studies, A/B testing, and product sense — everything you need for DS interviews at top tech companies.

What DS Interviews Actually Test

Data scientist interviews at big tech are broader than most candidates expect. They test five distinct skill areas: SQL and data manipulation, statistics and probability, machine learning theory and application, A/B testing and experimentation design, and product/business sense. Most companies test all five; the weighting varies. Meta and LinkedIn weight product sense heavily. Google and Amazon weight ML theory more. Stripe and Airbnb emphasize experimentation design.

Knowing which areas a specific company emphasizes is half the battle. LeakCode lets you filter DS interview reports by company and seniority so you can calibrate before investing weeks in prep.

SQL: The Baseline Everyone Must Clear

Nearly every DS interview includes at least one SQL round, and it's often an early-stage filter. The questions range from basic aggregations to window functions, self-joins, and multi-step analytical queries. You need to be comfortable with: GROUP BY and aggregation, HAVING vs WHERE, subqueries vs CTEs, window functions (ROW_NUMBER, RANK, LAG, LEAD, SUM OVER PARTITION BY), and query optimization basics.

The most common failure point is not knowing window functions well. If you can't write SUM(revenue) OVER (PARTITION BY user_id ORDER BY date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) fluently, prioritize this above all else.

Statistics and Probability

Statistics questions at DS interviews are often deliberately ambiguous to test whether you think carefully about assumptions. Core topics: probability (Bayes' theorem, conditional probability), hypothesis testing (p-values, Type I/II errors, statistical power), confidence intervals, distributions (normal, binomial, Poisson), and the Central Limit Theorem.

A frequently underestimated topic: variance and bias tradeoffs in ML models, and why a high-accuracy model on training data can fail in production. Understanding overfitting, regularization (L1/L2), and cross-validation is expected even for interviews with minimal ML focus.

A/B Testing and Experimentation

This is where DS interviews at product companies diverge from academia. You'll be asked to design experiments, identify flaws in existing experiment setups, and interpret results. Common pitfalls interviewers probe for: peeking (stopping early when results look significant), novelty effects, network effects violating SUTVA, incorrect randomization units (user vs cookie vs session), and Bonferroni corrections for multiple testing.

Practice walking through: "How would you measure whether feature X is working?" Starting with "what metric am I optimizing?" and ending with "what could go wrong with this experiment design?" is the structure interviewers want to see.

Machine Learning Case Studies

ML case studies test whether you can apply theory to real problems. A typical prompt: "Design a recommendation system for [feature]" or "How would you build a fraud detection model?" The interviewer is evaluating your problem decomposition, feature engineering instincts, model selection reasoning, and awareness of production concerns (latency, explainability, data drift).

Don't jump to deep learning for everything. Showing awareness of when a logistic regression or gradient-boosted tree is the right tool (and why) signals stronger ML maturity than defaulting to neural networks.

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