Data Driven Recommendations and Impact Questions

Covers the end to end practice of using quantitative and qualitative evidence to identify opportunities, form actionable recommendations, and measure business impact. Topics include problem framing, identifying and instrumenting relevant metrics and key performance indicators, measurement design and diagnostics, experiment design such as A B tests and pilots, and basic causal inference considerations including distinguishing correlation from causation and handling limited or noisy data. Candidates should be able to translate analysis into clear recommendations by quantifying expected impacts and costs, stating key assumptions, presenting trade offs between alternatives, defining success criteria and timelines, and proposing decision rules and go no go criteria. This also covers risk identification and mitigation plans, prioritization frameworks that weigh impact effort and strategic alignment, building dashboards and visualizations to surface signals across HR sales operations and product, communicating concise executive level recommendations with data backed rationale, and designing follow up monitoring to measure adoption and downstream outcomes and iterate on the solution.

MediumTechnical

0 practiced

Explain three methods you would use to strengthen causal inference from observational data (not experiments) for estimating the effect of a product change. For each method, describe the core assumption, diagnostics you would run to check the assumption, and a short example of when that method is appropriate.

MediumTechnical

0 practiced

You have a backlog of analytics requests from Product, Sales, and Ops. Propose a prioritization framework that weighs impact, effort, and strategic alignment. Provide an example scoring rubric with 4-5 dimensions, how you'd estimate scores, and how you'd incorporate stakeholder urgency or compliance requirements.

EasyTechnical

0 practiced

Define clear go/no-go criteria for a 6-week pilot of a new onboarding flow intended to increase Day-7 retention. Specify primary metric, minimum detectable improvement, secondary safety metrics, statistical thresholds, and what actions to take at each possible outcome.

MediumTechnical

0 practiced

You are asked to detect and quantify selection bias in a user survey used to inform product recommendations. Explain how you would compare survey respondents to the overall user base, what weighting or adjustment methods you might apply, and how you'd report the residual risks in the survey-based insights.

MediumTechnical

0 practiced

Design a one-page product launch dashboard for senior leadership to monitor a new referral program during its first month. List the key visualizations/cards, the primary and secondary metrics, alert thresholds, data latency requirements, and one methodology for anomaly detection.

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