Product Metrics and Health Questions

Designing and using product specific metrics to measure user experience product health and business impact. Topics include identifying a north star metric and supporting metrics at company product and feature levels, measuring activation adoption engagement retention daily active users and monthly active users feature adoption rates and time to value, using product telemetry experimentation and funnel analysis to measure feature impact, and connecting product metrics to monetization and strategic objectives. Candidates should be able to propose metrics for new features justify trade offs instrument tracking and explain how product metrics inform prioritization roadmap and stakeholder alignment.

MediumTechnical

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Write pseudocode (or Python) to compute retention cohorts: Day-0 signups grouped weekly and the percentage still active on days 1, 7, 30. Describe assumptions about event schema and how to scale the computation for millions of users.

EasyTechnical

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A/B experiment shows feature A increases engagement by 8% but reduces conversion to paid plan by 4% (p < 0.05). As a data scientist, how would you advise the product and leadership teams? Outline the analysis, recommendation options, and any further experiments you would run.

HardTechnical

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Explain how privacy-enhancing techniques (differential privacy, k-anonymity, aggregation) affect the design and interpretability of product metrics like retention and DAU. Provide examples of metric distortions and how to communicate uncertainty to stakeholders.

MediumTechnical

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Explain how to use uplift modeling to measure the causal effect of a personalization feature on engagement. Describe the data you need, model choices, necessary randomization, and pitfalls like selection bias and interference.

EasyTechnical

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You observe a sudden 15% drop in weekly active users for one product line. As the data scientist on-call, outline the first 6 diagnostic steps you would take to identify root causes, including data validation checks and product/experiment considerations.

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