Lead Applied Researcher - Dynamic, Bid-Based Pricing

inDrive; City Of London; On-site; GBP 110,000 - 150,000; Full time

Benefits offered by this job

Global opportunities

Mentoring programs

International teams

Language courses

Job summary

inDrive Lead Applied Researcher — Pricing leads the design of dynamic pricing for our marketplace, balancing supply, demand, and outcomes under uncertainty. You will develop pricing as a feedback-driven decision system shaped by direct negotiation between riders and drivers.

The role seeks 3+ years in data science or pricing, with strong statistics, optimization, and Python/SQL proficiency to productionize models and run real-time experiments.

Qualifications

• 3+ years in data science or applied research in pricing or marketplaces.
• Strong background in statistics, optimization and causal inference.
• Production-grade ML experience and ability to move from problem to implementation.

Responsibilities

• Design and refine dynamic pricing policies and elasticity models.
• 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 with engineering for scalability and robustness.
• Monitor performance, stability, and long-term effects of pricing systems.
• Contribute to methodological choices in optimization, control, or RL-inspired approaches.

Skills

Data Science

Applied Research

Pricing Systems

Statistics

Optimization

Causal Inference

Production deployment

Tools

Python

SQL

Production ML

Job description

inDrive Lead Applied Researcher — Pricing leads the design of dynamic pricing for our marketplace, balancing supply, demand, and outcomes under uncertainty. You will develop pricing as a feedback-driven decision system shaped by direct negotiation between riders and drivers.

The role seeks 3+ years in data science or pricing, with strong statistics, optimization, and Python/SQL proficiency to productionize models and run real-time experiments.