Experimentation Velocity and Iteration Mindset Questions

Demonstrate a bias toward rapid experimentation and continuous iteration. At junior level, this means showing comfort with speed-over-perfection thinking: running small, fast experiments to learn quickly rather than lengthy planning cycles. Discuss how you prioritize learning speed, discuss experiments that 'failed' but taught you valuable lessons, and show examples of iterating rapidly based on data. Mention tools and processes that enabled experimentation velocity (e.g., running 3-4 tests per week, using no-code testing tools, rapid prototyping). Show that you view marketing as a series of controlled experiments rather than campaigns executed once.

HardTechnical

0 practiced

Explain counterfactual policy evaluation methods that estimate experiment or policy outcomes without full randomized experiments. Cover inverse propensity scoring (IPS), weighted importance sampling, doubly robust estimators, and model-based approaches. Discuss practical production pitfalls such as logging propensities, support mismatch, high-variance estimates, and when counterfactual methods are appropriate.

HardTechnical

0 practiced

Design a hierarchical Bayesian modeling approach to borrow strength across related experiments (for example: similar feature variants tested across different cohorts) to accelerate learning. Specify the model hierarchy, suggested priors, how you'd perform inference (e.g., MCMC vs variational inference), how to check model assumptions, and how to surface pooled results to non-technical stakeholders.

MediumSystem Design

0 practiced

Your existing experiment platform only supports fixed A/B tests. Describe how you would add support for multi-armed bandits: changes to data collection and logging, an online policy engine, requirements for offline evaluation and logging propensities, UI changes for experiment owners, and guardrails to prevent runaway allocations. Also explain how to support both an exploratory phase and an exploitative phase.

MediumTechnical

0 practiced

Explain multiple hypothesis testing and the false discovery rate (FDR). For a growth team running hundreds of tests per month, propose a practical pipeline to control FDR while preserving velocity: include choices of correction methods, how to pre-register experiments, and how to report discoveries to stakeholders.

MediumTechnical

0 practiced

Case study: A new recommendation model yields +8% revenue in offline evaluation but no lift in production A/B tests. Create a prioritized investigation plan that includes data preprocessing checks, feature drift detection, offline-vs-online metric mismatch analysis, logging/serving differences, randomization and sample checks, and experiments (e.g., shadow deploys) to isolate the cause.

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