Product & Business Sense for Data Science Interviews — From Metric Design to Marketplace Trade-offs

Product and business sense is the section where most data scientists struggle in interviews — not because they lack technical skills, but because they jump to metrics and models before understanding the problem.

This guide teaches you to reason like a product partner, not just an analyst:

✅ The product DS mindset — how to connect analysis to a decision and a recommendation

✅ The most important habit: how to close every answer with one clear recommendation

✅ 4-sided marketplace thinking — customers, shoppers, retailers, platform — and how features affect all sides

✅ Metric design — primary metrics, guardrails, secondary metrics, leading vs lagging indicators

✅ Metric tree decomposition — how to break a metric before diagnosing a drop

✅ The 6-step metric drop investigation framework — including platform, geo, new vs existing user segmentation

✅ 5 real practice questions with model answers — delivery time investigation, DAU drop, shopper location feature, feature prioritization, shopper quality metrics

✅ RICE prioritization — all 4 dimensions including the ones candidates most often miss

✅ Causal vs predictive questions — and the campaign targeting trap

✅ The 60-second answer template

16 pages. Built from real practice questions before a senior DS screen.