This was a lot to hold in your head at once.
Start by clarifying the business problem and aligning on a single primary success metric with guardrail metrics, then structure your answer as a logical narrative from problem definition to experiment design to risk assessment. Emphasize iterative learning and data-driven decision-making, showing how you'd validate assumptions with an MVP before investing in complex ML solutions.
Pro tip: Anchor your answer in Amazon's leadership principles: explicitly tie your approach to Customer Obsession (defining success from the customer's perspective) and Invent and Simplify (favoring simple baselines over complex ML). Also, mention how you'd use Amazon's internal experimentation tools like Weblab or CloudWatch for instrumentation.
Clarify the business problem and identify the primary success metric (e.g., conversion rate, engagement) along with guardrail metrics (e.g., latency, error rates). List constraints such as budget, timeline, and technical feasibility.
Brainstorm potential causes and solutions, then prioritize hypotheses based on impact and effort. Determine what data and instrumentation are required to measure the metrics, including logging, tracking, and existing data sources.
Outline a minimal viable experiment with control and variant groups, specifying randomization unit, sample size, duration, and success criteria. Ensure the experiment is statistically powered and addresses potential confounders.
Compare simple baselines (e.g., rule-based, heuristics) against ML approaches, considering trade-offs in complexity, interpretability, and time-to-market. Recommend starting with the simplest solution that could work.
Call out risks such as data leakage, novelty effects, technical debt, or ethical concerns, and propose mitigations like holdout groups, phased rollouts, or monitoring dashboards.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.