##### Scenario You are tasked with building a dynamic-pricing system for Lyft (a two-sided ride-hailing marketplace) that balances rider demand, rider ETA/conversion, driver supply and earnings, company revenue, and fairness. ##### Question Design Lyft's dynamic-pricing algorithm. Address the following: 1. **Features & data sources.** What real-time, historical, and contextual data and features would you include (rider demand signals, driver supply/proximity, location, time, weather, traffic, events, elasticity signals, etc.)? 2. **Model architecture.** What modeling components and architecture would you use (e.g., demand forecasting, supply/acceptance response, ETA, conversion, elasticity estimation, and a control/optimization layer)? Discuss model choices such as gradient-boosted trees vs. RL/contextual bandits and their pros and cons. 3. **Trade-offs & objective.** How would you formulate the objective and balance trade-offs between rider demand, driver supply, ETA/service quality, revenue, price stability, and fairness? 4. **Fairness, safety & constraints.** How would you add surge caps, fairness/parity constraints, smoothing, and regulatory compliance? 5. **Elasticity estimation.** How would you estimate price elasticity, including causal identification? 6. **Experimentation & operations.** How would you validate the system (offline and online experiments) and operate it reliably (serving, monitoring, fallbacks)? ##### Hints Consider separating prediction (what will happen) from control (what price to set). Think about real-time demand/supply, historical trends, weather, traffic, events, elasticity signals, model choice, monotonicity constraints, surge caps, and guardrails.