Data Analysis and Insight Generation Questions

Ability to convert raw data into clear, evidence based business insights and prioritized recommendations. Candidates should demonstrate end to end analytical thinking including data cleaning and validation, exploratory analysis, summary statistics, distributions, aggregations, pivot tables, time series and trend analysis, segmentation and cohort analysis, anomaly detection, and interpretation of relationships between metrics. This topic covers hypothesis generation and validation, basic statistical testing, controlled experiments and split testing, sensitivity and robustness checks, and sense checking results against domain knowledge. It emphasizes connecting metrics to business outcomes, defining success criteria and measurement plans, synthesizing quantitative and qualitative evidence, and prioritizing recommendations based on impact feasibility risk and dependencies. Practical communication skills are assessed including charting dashboards crafting concise narratives and tailoring findings to non technical and technical stakeholders, along with documenting next steps experiments and how outcomes will be measured.

HardTechnical

0 practiced

With limited engineering capacity, propose a prioritization framework to score incoming analytical work (ad-hoc requests, dashboards, experiments, data-quality fixes). Define scoring dimensions (impact, effort, risk, dependencies), provide example scores for 3 hypothetical requests, and explain how you'd operationalize the intake and periodic review with stakeholders.

MediumTechnical

0 practiced

Design an A/B test to evaluate a new checkout flow expected to increase conversion by 5%. Specify the primary metric, guardrail metrics, assumptions for sample size calculation (baseline conversion, power, alpha), how you'd prevent peeking, and how you'd handle users who see both variants (crossovers).

HardTechnical

0 practiced

You have only 3 months of historical data but need to estimate 12-month LTV for a new cohort. Propose a method to extrapolate LTV: describe modeling assumptions, parametric or nonparametric approaches, uncertainty quantification (confidence intervals), and how you would validate predictions when more data becomes available.

EasyTechnical

0 practiced

Write a SQL query or describe the approach to compute weekly retention rates for cohorts defined by user signup week. Input tables: `users(user_id, signup_date)` and `events(user_id, event_date)`. Return a cohort table where each row is signup_week and retention for week 0..12 as percent of cohort active in that week. Explain handling of users with multiple events per week.

HardTechnical

0 practiced

In an A/B test the primary metric shows no lift but revenue increased significantly for a small user subgroup. Explain how you would analyze heterogeneous treatment effects, correct for multiple comparisons, and make a rollout decision that balances statistical rigor and business impact.

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