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.
MediumTechnical
0 practiced
Explain uplift (treatment-effect) modeling and how it differs from standard predictive modeling. Describe common use-cases in marketing or personalization, model architectures (two-model approach, T-learner, S-learner, X-learner), evaluation metrics (Qini, uplift curve), and deployment pitfalls to avoid.
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.
MediumTechnical
0 practiced
You are asked to design an A/B test expecting to increase conversion from 3% to 3.5%. Describe how to compute required sample size and experiment duration. Include assumptions (baseline rate, minimum detectable effect, desired power, alpha), show the formula or Python/R code you'd use, and discuss how variance and concurrent experiments affect duration.
MediumTechnical
0 practiced
Given a table 'events(user_id, event_type, occurred_at TIMESTAMP, signup_date DATE)', write an ANSI/Postgres SQL query that computes weekly cohort retention: cohorts by signup week (YYYY-WW), week_number (0..4), users_in_cohort, retained_users, retention_rate. Show how you deduplicate users per week and handle weeks with no events. State assumptions about timezone normalization and deduplication.
HardTechnical
0 practiced
Explain Simpson's paradox with a concrete analytics example where two product segments both improved conversion but the aggregated conversion decreased. Show how to detect Simpson's paradox in your data, what diagnostics to run, and how to present the correct stratified interpretation to stakeholders to avoid misleading aggregated conclusions.
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