Frontier AI Labs Data Scientist Interview Questions
The questions to prepare for a Frontier AI Labs Data Scientist interview. Questions from real interview reports rank first. Updated daily.
Define a meaningful engagement metric for a retail analytics mobile application and explain how to validate it.
Evaluates ability to define product metrics for AI features.
Choose sample size and runtime by combining baseline rate, MDE, alpha, power, and expected traffic.
Explain how to evaluate whether an A/B test result is statistically significant and how to interpret the result.
Compute each user's seven-day activity average and compare it with the corresponding rolling average from the prior week.
Outline the first checks to diagnose a sudden drop in a core product metric, starting with data quality, scope, and decomposition.
Identify the main pitfalls that can distort A/B test interpretation and explain how to guard against them.
Explain how to profile, clean, and standardize missing or dirty data before analysis.
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Calculate monthly cohort retention using date-based cohorts, aggregation, and window functions.
Use aggregation and a window function to rank active players by completed purchase spend, including players with no qualifying purchases.
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