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inDrive is seeking a Lead Applied Researcher – Pricing to design and evolve dynamic pricing systems for its marketplace platform. 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, inDrive operates on a unique bid-based pricing model where prices are shaped through direct negotiation between riders and drivers, creating a fundamentally different optimization problem that requires advanced approaches to modeling user behavior, incentives, and marketplace dynamics.
Key responsibilities include:
- 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)
Requirements:
- 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
