Analytical Background Questions
The candidate's analytical skills and experience with data driven problem solving, including statistics, data analysis projects, tools and languages used, and examples of insights that influenced product or business decisions. This covers academic projects, internships, or professional analytics work and the end to end approach from hypothesis to measured result.
EasyTechnical
0 practiced
Explain precision, recall, specificity, F1 score, and ROC-AUC for a binary classifier. For an AI use-case like spam detection or fraud detection, discuss when each metric is most appropriate, trade-offs between precision and recall, and how threshold selection impacts production behavior (false positives vs false negatives).
EasyTechnical
0 practiced
You're preparing an exploratory data analysis (EDA) for user engagement over time across product cohorts. Outline specific visualizations, summary tables, and data transformations you would produce for a stakeholder slide deck. Explain why each element helps and how you'd highlight actionable insights and uncertainty.
HardTechnical
0 practiced
Offline evaluation of recommender algorithms using historical logs is biased because shown items were not random. Propose analytic corrections and experimental designs to obtain unbiased offline estimates (e.g., inverse propensity scoring, randomized exploration logging, doubly robust estimators). Discuss how to implement these approaches at scale and the trade-offs between variance and user experience.
MediumTechnical
0 practiced
Implement in Python a function bootstrap_median_ci(data: Sequence[float], n_bootstrap: int = 10000, alpha: float = 0.05) -> (float, float) that returns a bootstrap confidence interval for the median. Explain your choice of bootstrap approach (percentile vs bias-corrected) and discuss computational trade-offs and how to speed up bootstrapping for large datasets.
HardTechnical
0 practiced
Multiple experiments target overlapping user populations and traffic is limited. Describe how to allocate traffic across concurrent experiments to retain statistical power while minimizing harmful interactions. Discuss options (full-factorial, independent buckets with exclusion, hierarchy/prioritization), how to model interaction effects, and how to prioritize experiments for expected business impact.
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