Data Driven Prioritization Questions

Using data and metric thinking to prioritize initiatives and decide what to build next. This covers selecting one to a few primary metrics to focus on for a specific growth or product challenge, weighing trade-offs between competing business goals such as acquisition versus retention or speed versus quality, and applying pragmatic approaches to measurement when perfect data is not available. Candidates should demonstrate how they translate business goals into measurable success criteria, estimate impact and effort, use simple models or scoring to rank opportunities, and explain how they will track and communicate progress and tradeoffs to stakeholders.

MediumSystem Design

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With limited engineering bandwidth, describe the metrics, visualization elements, and interaction patterns you would include in a prioritization dashboard for PMs that combines product value, implementation complexity, and data availability; explain how you would normalize scores and allow PMs to filter and compare scenarios.

HardSystem Design

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Design the analytics instrumentation and data model required to support data-driven prioritization at scale, including a feature catalog, standardized event schema with experiment metadata, metrics lineage, and APIs for PMs to query impact and confidence. Mention how you would handle idempotency, backfills, and schema evolution.

MediumTechnical

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Explain how propensity score matching can be used to estimate feature impact from observational rollout data; describe the assumptions required, the implementation steps, diagnostics to check balance and overlap, and practical pitfalls and alternatives when matching is not viable.

MediumTechnical

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Describe how to set and communicate experiment stopping rules that balance speed versus false positive rate, covering fixed-horizon tests, sequential testing, and pragmatic approaches to early stopping; include how to explain alpha spending or Bayesian thresholds to non-technical stakeholders.

HardTechnical

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Build a Bayesian decision model to choose between two mutually exclusive initiatives; define priors for each initiative's effect, specify likelihood and loss function (including opportunity cost), state a decision rule to pick the initiative with maximum expected utility, and demonstrate with a numeric example showing posterior updates and the resulting decision.

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