This question is basically five questions stitched together and I did not handle that gracefully at first.
Structure your answer around a clear, repeatable process: clarify requirements, scope an MVP, communicate progress, make data-driven trade-offs, and plan for iteration. Emphasize collaboration with the PM and a focus on delivering value quickly while managing expectations. Use specific examples from your experience to illustrate each step.
Pro tip: Proactively define success metrics and a fallback plan upfront; this shows you think beyond the immediate task and can manage risk, which is highly valued in data science roles.
Ask the PM targeted questions to uncover the underlying goal, success metrics, and constraints. Use techniques like the '5 Whys' to dig deeper and align on what 'rank products' actually means.
Propose a minimal viable product that delivers core value quickly, such as a simple heuristic or existing model, and outline what's explicitly out of scope. Get buy-in from the PM on this reduced scope.
Set up regular check-ins and use a shared document to track decisions, progress, and blockers. Proactively flag risks and adjust expectations as you learn more.
Prioritize based on impact vs. effort, using data to inform decisions. For example, choose a simpler model that can be deployed faster over a complex one that might perform slightly better.
Define what failure looks like and have a contingency plan, such as A/B testing, fallback to a baseline, or a rapid iteration cycle. Communicate this plan to stakeholders early.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.