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.

HardTechnical

29 practiced

You suspect unobserved confounding in an observational pricing experiment. Explain how you would use an instrumental variable (IV) or a regression discontinuity (RDD) design to estimate causal effects. Provide a concrete IV candidate or discontinuity example that could plausibly apply in a marketplace scenario and list the assumptions needed.

HardTechnical

33 practiced

You manage a generative recommender that can produce sensitive or harmful suggestions. Propose a measurement and mitigation plan to quantify and reduce risk. Include automated detection, human-in-the-loop review rates, adversarial testing, user reporting metrics, and an incident response playbook.

EasyTechnical

31 practiced

You're asked to evaluate whether to recommend building a personalized article recommendation feature for a news app. Frame the problem end-to-end: state the product objective, propose 2–3 primary and 3–5 secondary metrics (quantitative and qualitative), define success criteria (absolute and relative), outline a 3-month measurement timeline, and list key assumptions and risks that would change your recommendation.

HardTechnical

42 practiced

Compare the doubly-robust estimator, inverse-propensity weighting (IPW), and outcome regression for estimating ATE. Discuss conditions under which each is unbiased, their variance trade-offs, and sample size implications when using flexible machine learning models for nuisance components.

MediumSystem Design

28 practiced

Design a dashboard to monitor model drift (population shift and concept drift) for a recommender system. Specify which metrics to track (statistical and business), the sampling frequency, alerting thresholds, and actions when a drift alert fires.

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