Business Problem Solving and Recommendations Questions

Frameworks and skills for taking ambiguous business questions through analysis to clear, actionable recommendations. Includes decomposing complex problems into analyzable components, identifying key drivers, selecting focused analyses, synthesizing data backed findings, and articulating specific next steps and implementation considerations. Emphasizes communicating recommendations in business terms, estimating potential impact when possible, acknowledging trade offs and limitations, prioritizing among multiple actions, and tailoring communication to different stakeholders. Covers translating research or analytic results into feasible product or operational changes and defending choices with evidence.

HardTechnical

73 practiced

Your model's uplift is concentrated in high-value users and leadership is concerned about fairness across segments. Propose an approach to balance expected revenue with fairness constraints: define fairness metrics relevant to business context, show how to formulate a constrained optimization (maximize revenue subject to fairness thresholds), and outline how you would implement and monitor such a solution.

MediumSystem Design

55 practiced

You receive raw event logs at ~200M events/day with: user_id, event_name, timestamp, and payload JSON. Design an ETL/analytics pipeline to compute DAU/MAU, cohort analyses, and support ad-hoc SQL queries. Include storage choices (data lake vs OLAP), partitioning and clustering strategy, batch vs streaming trade-offs, pre-aggregation strategy, retention policy, and high-level cost vs performance considerations.

HardTechnical

73 practiced

Explain how you would quantify uncertainty around an expected revenue uplift estimate produced by an analytic model. Describe methods to compute confidence intervals that account for sampling variability and model-selection uncertainty (e.g., bootstrap, cross-validation-based intervals, Bayesian credible intervals), and how to present this uncertainty to stakeholders.

MediumTechnical

62 practiced

Interpret the following uplift table (conversion rates) and recommend next steps. Table:

| segment | control_conv | treatment_conv | control_n | treatment_n ||-------------:|-------------:|---------------:|----------:|-----------:|| high-value | 0.10 | 0.15 | 2000 | 2000 || low-value | 0.03 | 0.035 | 5000 | 5000 |

Is the uplift meaningful, where would you focus efforts, and what statistical checks would you run before recommending rollout to all users?

MediumTechnical

73 practiced

You have a predictive model identifying features correlated with higher customer spend. The product team wants to act on those correlations (e.g., change pricing or offer bundles). Explain when feature importance is sufficient for action and when you must estimate causal effects. Recommend specific causal methods to test pricing changes and how you would validate them before rollout.

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