Local Demand-Based Dynamic Pricing Model

Business Context

You’re interviewing for an ML role on the Pricing team at RideNow, a ride-hailing marketplace operating in 35 North American cities with ~4.5M weekly active riders and ~900K active drivers. Pricing is currently set by a rules-based “surge” system using coarse geofences and simple demand/supply ratios. Leadership believes the system leaves 3–6% weekly gross bookings on the table: it underprices in some micro-areas during events (leading to long ETAs and cancellations) and overprices in others (hurting conversion).

Your task is to design a model that optimizes price multipliers based on local demand at a fine spatiotemporal resolution, while respecting marketplace constraints (driver supply, rider experience, and fairness).

Dataset

You are given 12 months of historical marketplace logs aggregated to (city, zone_id, 15-minute bucket).

Target decision: choose a price multiplier m for each (zone, time) for the next 15 minutes.

Success Criteria

You will be evaluated offline and via an online A/B test plan:

1. Offline objective: maximize expected gross profit per 15-min bucket (revenue − driver incentives − support costs from cancellations).
2. Guardrails (must not regress):
   • Rider conversion rate drop ≤ 0.5% relative
   • Cancellation rate increase ≤ 0.2 pp
   • ETA p90 increase ≤ 30 seconds
3. Business target: demonstrate an estimated +1.5% to +3.0% lift in gross profit in backtests on holdout months.

Constraints

• Latency: real-time inference under 50 ms p95 per zone request; batch scoring for all zones every 5 minutes is acceptable.
• Interpretability: pricing ops needs to understand major drivers (events/weather/commute patterns) and have override controls.
• Data leakage: must avoid using post-decision outcomes (e.g., completed_rides) when predicting next bucket.
• Policy bias: historical data was generated by the current surge algorithm; the model must handle counterfactual pricing concerns.
• Safety/fairness: avoid systematically higher multipliers in protected neighborhoods without demand justification; require monitoring by neighborhood clusters.

Deliverables

1. Formulate the problem mathematically: what do you predict (demand, elasticity, or profit) and how does that map to an optimal multiplier?
2. Propose a modeling approach that can learn local price-response from logged data (and explain assumptions).
3. Describe feature engineering for spatiotemporal demand, including cold-start zones and event spikes.
4. Provide an evaluation plan: offline metrics, backtesting protocol, and an online A/B test design with guardrails.
5. Outline a production deployment: data pipeline, inference architecture, retraining cadence, and monitoring (drift, fairness, and KPI regressions).