You are a Data Scientist supporting a “biker” (delivery rider) product/project for a food-delivery platform. An interviewer gives only a short description of the project and asks you to explain how you would approach it as a DS. ## Prompt 1) **Goal framing:** What clarifying questions would you ask, and how would you translate the business goal into measurable objectives? 2) **Business process & data:** Describe the end-to-end biker workflow (dispatch/assignment → accept/decline → travel to restaurant → pickup → travel to customer → dropoff → post-delivery). For each step, list what data/events you would expect to capture and how they map to metrics. 3) **Metrics:** Propose: - Primary success metric(s) - Diagnostic metrics (to explain why the primary metric moved) - Guardrail metrics (to prevent harm) 4) **Causal inference / evaluation:** If you cannot run a perfect randomized experiment immediately, how would you estimate impact and reduce bias (confounding, seasonality, selection effects)? 5) **Misclassification + precision/recall tradeoffs:** In this context (e.g., detecting problematic deliveries/riders/orders or triggering interventions), explain where false positives vs false negatives matter, and how you’d set thresholds. 6) **A/B testing design:** If city-level clustering is one option, what other randomization units could you use (order, rider, customer, restaurant, zone, time-based switchback, etc.)? For each, discuss pros/cons, interference/spillover risks, and when you would choose it.