Lead Applied Researcher – Pricing

We are looking for a Lead Applied Researcher — Pricing to design and evolve dynamic pricing systems for our marketplace. You will develop pricing as a feedback-driven decision system, balancing supply, demand, and business outcomes under uncertainty and delayed signals. Unlike traditional surge-based pricing, our marketplace operates on a unique bid-based pricing model, where prices are shaped through direct negotiation between riders and drivers. This creates a fundamentally different optimization problem, requiring advanced approaches to modeling user behavior, incentives, and marketplace dynamics.

- Design and improve dynamic pricing policies, including elasticity and supply/demand response modeling - Frame pricing as a decision system under uncertainty and delayed feedback - Develop, test, and deploy real-time pricing algorithms - Design and analyze A/B experiments to evaluate pricing impact - Productionize models in collaboration with engineering, ensuring scalability and robustness - Monitor system performance, including stability, feedback effects, and long-term behavior - Contribute to methodological choices (e.g., optimization, control, RL-inspired approaches)

- 3+ years of experience in Data Science, Applied Research, or algorithmic product optimization, preferably in marketplaces or pricing systems - Strong background in statistics, optimization, and causal inference - Proven ability to move from problem formulation to production-grade solutions - Proficiency in Python, SQL, and experience with production ML systems

Nice to Have

- Experience with economic modeling (elasticity, auctions, policy design) - Familiarity with control systems, RL, or online optimization - Experience working with real-time, high-load systems