EarnIn Data Scientist Interview Questions
The questions to prepare for a EarnIn Data Scientist interview. Questions from real interview reports rank first. Updated daily.
Define a success metric for a new feature that captures real user value, not just raw usage.
Diagnose a sudden decline in EarnIn engagement by separating data issues, product changes, user behavior, and external causes.
Explain how to profile, clean, and standardize missing or dirty data before analysis.
Calculate three-day rolling user activity averages and classify behavior trends with SQL window functions.
Identify the main pitfalls that can distort A/B test interpretation and explain how to guard against them.
Determine A/B test sample size from baseline rate, MDE, significance level, and power, then validate the calculation through simulation.
Diagnose a post-release KPI drop by separating instrumentation issues from real behavior changes and tracing the problem through the metric hierarchy.
Explain what statistical significance means and why it matters when interpreting experimental or analytical results.
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