A practical question bank for hiring data scientists — statistics, modeling, evaluation, and experimentation, plus the judgement to know when a model is helping the business and when it is just impressive. Use these to find a scientist, not a Kaggle competitor.
The hardest part of hiring a data scientist is that the flashy skill — training models — is rarely the bottleneck on the job. The bottleneck is judgement: framing a fuzzy business problem as a measurable question, picking an evaluation metric that matches what the business actually cares about, knowing when a simple model beats a complex one, and noticing when a result is too good to be true. Many candidates can recite the bias-variance trade-off but cannot explain why their cross-validation score did not survive contact with production. The questions below are grouped so you can test the fundamentals (statistics, modeling, evaluation) and then the applied reasoning that separates a scientist from a notebook operator. Push hard on evaluation: ask why accuracy is misleading on imbalanced data, what precision and recall mean for a specific business decision, and how they would detect data leakage. Push equally hard on experimentation, because most real impact comes through well-run A/B tests, not exotic architectures. And reserve real weight for communication — a data scientist who cannot explain a result to a skeptical product manager will see their work ignored regardless of how good the model is. Reward candidates who ask clarifying questions, reach for the simplest method that works, and talk in terms of business outcomes rather than leaderboard scores.
Test fundamentals with a couple of statistics and evaluation questions, then spend most of the time on an applied scenario: frame a business problem, pick a metric, and defend the trade-offs. The strongest signal is a candidate who reaches for the simplest approach that works and ties everything back to a business decision.
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