Microsoft — Bing — Product Case Questions

Role context: Senior Data Scientist, Bing Growth & Experimentation (Microsoft AI) · Est. study time: 55 min · 5 questions

How to approach product cases here

Every case is one move in a single decision chain: understand the business → decompose into a data problem → choose the metric or method → name the bias and trade-off → land on a decision and an action. A strong answer walks that chain and survives the follow-ups.

Three things make Bing cases distinctive:

• Success is unobservable and clicks lie. There's no purchase event; a no-click can mean "answered on the page" (good abandonment) or "gave up." Decompose into successful sessions, and never read falling CTR as failure.
• Distribution is a confound. Most usage swings come from Edge/Windows defaults, not preference — so "growth" must be tested for durability (retention), and many of the biggest changes (default/market rollouts) can't be A/B-tested at all.
• Everything is a guardrailed trade-off. Ad revenue vs experience, AI answers vs ad clicks and publisher traffic, relevance vs latency (tens of ms measurably hurt engagement). The senior move is naming the guardrail and the exchange rate, not picking a side.

This role (experimentation + metrics + forecasting) gets probed across diagnose, measure-success, launch-or-not, measuring impact without an experiment, and forecasting — so the five below span all of those.

Each answer is staged in three parts: a structured overview, an in-depth investigation with the numbers and charts you'd pull, and a verbal pitch of how to say it in the room.