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.

EasyTechnical

0 practiced

Describe the difference between absolute lift and relative lift when reporting experiment results. Provide examples where absolute lift is more relevant than relative lift, and vice versa. How does baseline rate influence which you choose?

EasySystem Design

0 practiced

Sketch a simple dashboard layout (list items / short wireframe) to monitor recommendation model health and business KPIs for product managers and data scientists. Include top-level widgets, drilldowns, and typical alert thresholds. Explain why each widget matters and how it helps triage issues quickly.

HardTechnical

0 practiced

Implement a permutation test in Python to assess significance of an observed lift in average daily spend between two groups when users are not i.i.d. (e.g., repeated measurements over time). Provide code that preserves user-level grouping or temporal blocks and explain assumptions and computational optimizations.

MediumTechnical

0 practiced

Describe how you would use directed acyclic graphs (DAGs) to reason about confounders for estimating the causal effect of a recommendation on purchases. Provide a small DAG example (list of nodes and edges) and explain which variables you would control for and which you would avoid conditioning on (e.g., colliders).

MediumTechnical

0 practiced

Explain uplift (treatment effect heterogeneity) modeling and outline two model architectures you would implement to estimate individual treatment effects for personalization, e.g., two-model approach, uplift trees / causal forests. When would uplift models be preferred over including interaction terms in a standard predictive model?

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