inDrive

Global ride-hailing platform with fare negotiation

Lead Applied Researcher - Pricing

Applied Machine LearningUpdated on Nov 13

Full-timeLondon, UKNo salary listedRemote

About the Job

Requirements

Professional experience in Data Science, Applied Research, or algorithmic product optimization, preferably in marketplaces or pricing systems.
A strong background in statistics, optimization, and causal inference.
The ability to move from problem formulation to production-grade solutions.
Proficiency in Python and SQL, and experience with production machine learning systems.

Responsibilities

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, such as optimization, control, and reinforcement-learning-inspired approaches.

Desired Qualifications

Experience with economic modeling, including elasticity, auctions, and policy design.
Familiarity with control systems, reinforcement learning, or online optimization.
Experience working with real-time, high-load systems.

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)

Professional 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

About the Company

inDrive

Global ride-hailing platform with fare negotiation

inDrive operates a global mobility and urban services platform that connects passengers with independent drivers through a fare-negotiation system. Riders propose a trip price, and nearby drivers can accept, decline, or counteroffer, creating a peer-to-peer ride-hailing model. The service has expanded to intercity rides, delivery and cargo, groceries, advertising, and driver-focused fintech within its app. Unlike typical ride-hailing apps that use fixed fares, inDrive enables price negotiation and direct driver-rider interaction, and it pursues growth through a venture arm that funds ride-hailing-adjacent startups in emerging markets. This broader strategy aims to build a super-app for urban and gig-economy needs, with the goal of offering a wide range of mobility and urban services in 48 countries.

Company size: 10,001+
Company stage: Debt Financing
Total funding: $532M
Headquarters: Mountain View, CA
Founded: 2012

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