Ambiguous Product Scenario Navigation Questions
Develop your approach to product scenarios with incomplete information. Practice asking targeted clarifying questions (user context, business goals, constraints, success metrics), sizing the problem, and building a logical approach step-by-step. At Staff level, also articulate how you'd establish decision-making frameworks for the future so similar questions are resolved faster.
EasyTechnical
0 practiced
A PM requests a churn prediction model but gives no constraints. List the key feasibility questions you would ask (label definition, prediction horizon, actionability, data sources, SLA, acceptable false positive/negative rates). Explain how each question affects modeling choices and expected deliverables.
HardTechnical
0 practiced
A product change was rolled out to all users and engagement dropped. Randomization wasn't used. Propose a rigorous causal inference strategy to estimate the effect of the change: consider synthetic controls, difference-in-differences, interrupted time series, and matching. For each method, list required assumptions, diagnostics you'd run, and how you'd present the result and uncertainty to the PM.
MediumTechnical
0 practiced
Case study: PM proposes a 'premium badge' to monetize power users but gives no targets. As a data scientist, outline the metrics to evaluate product-market fit (activation/upgrade rate), short-term revenue impact, and long-term retention effects. Propose a safe rollout plan including experiment design and guardrail metrics to detect cannibalization.
MediumTechnical
0 practiced
You must build a churn model but the only available target is 'opened support ticket' — a noisy proxy for dissatisfaction. Describe methods to handle label noise (weak supervision, label smoothing, noise-robust loss), strategies to obtain higher-quality labels (active learning, small-scale surveys), and how to estimate the bias introduced by using this proxy.
HardSystem Design
0 practiced
Design an experiment catalog system and meta-analysis platform for the org so teams can capture hypotheses, instrumentation, variants, outcomes, and metadata. Specify data model (fields), APIs or UI capabilities, metadata governance, and how you'd enable meta-analysis across experiments (e.g., pooled effect estimation, priors reuse).
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