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