Design an Uber feature and analyze safety

You are interviewing for a Data Scientist summer internship at a ride-sharing marketplace. Part A: Product case Uber wants ideas for a new rider-facing or driver-facing feature, or a major improvement to an existing workflow, that would make the app meaningfully better. Pick one idea and explain: - the target user and pain point; - how the feature is expected to change user behavior; - the north-star metric, primary success metric, and guardrail metrics; - tradeoffs across rider experience, driver experience, safety, reliability, and revenue; - how you would test the feature, including the randomization unit, experiment duration, power or MDE considerations, and how you would handle marketplace interference or spillovers. Part B: Safety trend analysis Assume the monthly accident rate for one city is defined as accidents per 100,000 completed trips. A line chart shows that the accident rate increases sharply from June through November and then drops quickly after November. Describe how you would analyze this pattern. Be explicit about: - validating the metric definition and its numerator and denominator; - plausible hypotheses for the increase and subsequent decline; - what internal data cuts and external data you would investigate; - how to distinguish a true safety change from a reporting or measurement artifact; - what statistical or causal methods you would use and what follow-up actions you would recommend.