Lead Applied Researcher – Pricing

• 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

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

• Help us challenge injustice by creating fair choices for millions of people across 1100+ cities in 48 countries
• Develop your professional skills with access to mentoring, career consulting, and learning programs
• Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program
• Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects
• Work alongside people who take initiative, speak openly, and challenge themselves to grow
• Improve your language skills through co-financed courses and internal speaking clubs

Final benefits may vary depending on the location.

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)

Nice to Have