Analysis to Recommendation and Decision Framing Questions

Ability to move from analysis to a concise, justified recommendation and a pragmatic plan for decision and implementation. Candidates should lead with a clear recommendation or conditional decision, support it with evidence and trade offs, quantify expected business impact, estimate effort and time horizon, and state assumptions and limitations. The skill set includes proposing prioritized action plans and alternative options, anticipating objections, defining monitoring and rollback strategies, translating technical remediation or risk into business terms and measurable success metrics, and tailoring recommendations to stakeholder needs and constraints.

HardTechnical

0 practiced

Create a blueprint to measure the causal impact of an ML-driven personalization change on revenue. Include required instrumentation (events, exposure logs), counterfactual estimation methods (randomized experiments, synthetic controls, uplift modeling), sample size and power calculations for the primary outcome, a sensitivity analysis plan, and explicit assumptions and limitations.

HardSystem Design

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Architect a decision framework for running multi-armed bandit experiments in production that balances exploration-exploitation, expected revenue uplift, and business risk. Specify algorithm choices (e.g., epsilon-greedy, Thompson Sampling, contextual bandits), safety constraints, metrics to monitor, rollout strategy, and rollback triggers. Explain how you would incorporate business value estimates into the bandit objective.

HardTechnical

0 practiced

Your company is evaluating migrating large-scale model training from cloud-managed services to on-premise specialized hardware. Prepare a three-year total cost of ownership (TCO) comparison that includes capital expenses, operational costs, staffing, depreciation, opportunity cost of delayed features, and estimated performance gains. Provide a recommendation backed by numbers and describe key risks and contingencies.

MediumTechnical

0 practiced

You are evaluating whether to retrain a large production model on new data. Retraining is estimated to use 10,000 GPU-hours at $3/hour and is expected to yield a 3% relative lift in a revenue-driving metric. Provide a cost-benefit analysis that includes compute cost, expected incremental revenue (assume baseline monthly revenue $10M), time to implement, and a recommendation whether to retrain now or pursue cheaper alternatives. State assumptions and risks explicitly.

MediumTechnical

0 practiced

A product manager wants to add a confidence score to each product listing so the UI can downrank low-confidence items. What evidence would you require to recommend rollout? Describe an experiment to validate the effect on user behavior, expected metrics to track, and potential unintended consequences to watch for. Include a plan for calibration and fallback.

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