Using metrics and analytics to inform operational and strategic decisions. Topics include defining and interpreting operational measures such as throughput cycle time error rates resource utilization cost per unit quality measures and on time delivery, as well as growth and lifecycle metrics across acquisition activation retention and revenue. Emphasis is on building audience segmented dashboards and reports presenting insights to influence stakeholders diagnosing problems through variance analysis and performance analytics identifying bottlenecks measuring campaign effectiveness and guiding resource allocation and investment decisions. Also covers how metric expectations change with seniority and how to shape organizational metric strategy and scorecards to drive accountability.
HardTechnical
0 practiced
Discuss advanced causal inference methods applicable to AI product decisions: synthetic controls for aggregate counterfactuals, causal forests for heterogenous treatment effects, double/debiased machine learning for unbiased parameter estimation with ML nuisance functions, and front-door/back-door adjustments. For each method, state assumptions, required data structure, and an example product use case where it is appropriate.
EasyTechnical
0 practiced
Describe how to estimate cost per prediction for an online inference service. Given: GPU cost = $3/hour, one GPU can serve 300 requests/sec at average latency 50ms, and you expect 1,000,000 requests/day. Compute an approximate cost per prediction (assume full hours and 24/7 usage) and list practical optimizations to reduce cost (e.g., batching, quantization, model distillation).
HardSystem Design
0 practiced
Describe algorithms and system architecture to compute rolling KPIs (e.g., 7-day moving average retention, p95 latency, daily active users) over tens of billions of events per day in near-real-time. Discuss streaming frameworks (Apache Flink, Spark Structured Streaming), state backends, windowing strategies (sliding vs tumbling), approximate quantile algorithms, and trade-offs between consistency, latency, and cost. Also outline backfill and reconciliation approaches.
HardSystem Design
0 practiced
Design an enterprise metrics registry and governance platform for AI teams that supports centralized metric definitions (name, owner, SQL definition), lineage tracking, metric versioning, automated tests (sanity and coverage), access controls, and integration with CI/CD and dashboards. Describe components, metadata storage choices, APIs, onboarding flow, and a policy for metric deprecation and migration.
EasyBehavioral
0 practiced
Tell me about a time you used data to convince a non-technical stakeholder to change a product decision. Use the STAR format: describe the Situation, the Task you owned, Actions you took (metrics/analyses/dashboards used), and the Result. Explain how you communicated uncertainty and follow-up measurements.
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