inDrive

Global ride-hailing platform with fare negotiation

Senior Applied Researcher - Incentives

Applied Machine LearningUpdated on Nov 13

Full-timeLimassol, CyprusNo salary listedHybrid

About the Job

Requirements

Professional experience in Data Analytics, Data Science, Applied Research, or algorithmic product optimization, preferably in an incentive-driven environment.
Expertise in Machine Learning, behavioral modeling, and causal inference.
A solid foundation in mathematics and economics, including knowledge of elasticity models, incentive response dynamics, and retention analytics.
Advanced SQL and Python proficiency, experience working with large datasets, and the ability to translate research models into production-grade decision logic.
Experience with A/B testing frameworks, switchback experiments, and evaluating causal effects in networked systems with feedback loops.
Ability to collaborate closely with engineering teams and influence product decision-making.

Responsibilities

Develop and automate decision-making algorithms in incentives.
Design and implement incentive management systems as closed-loop feedback control systems to continuously optimize budget efficiency and marketplace balance.
Create and advance causal inference and uplift models, including meta-learners, to evaluate the true incremental value of rewards and target personalized offers.
Research and model marketplace participants’ non-linear responses to changing conditions, such as prices and incentive terms, and translate these insights into algorithmic strategies that drive user retention and lifetime value.
Translate research models into production-grade code, developing modular frameworks and monitoring systems to ensure algorithms operate reliably in real time.
Act as a strategic partner to product managers and engineers, drive the product roadmap through data-driven storytelling, and align algorithmic solutions with business goals.

We are looking for an expert who will develop and automate decision-making algorithms in the area of incentives. Your core focus will be to design and deploy adaptive data-driven mechanisms that determine which goal to set for which user, and what reward to offer, in order to maximize the incremental effect for every dollar invested.

Intervention Algorithm Automation: Design and implement incentive management systems as closed-loop feedback control systems to continuously optimize budget efficiency and marketplace balance.
Incremental Impact Maximization (Uplift Modeling): Create and advance causal inference and uplift models (e.g., meta-learners) to evaluate the true incremental value of rewards and target personalized offers.
Behavioral Modeling & Business Impact: Research and model the non-linear responses of marketplace participants to changing conditions (prices, incentive terms), translating these insights into algorithmic strategies that drive user retention and lifetime value (LTV).
End-to-End Development (Research-to-Production): Translate research models into production-grade code, developing modular frameworks and monitoring systems to ensure algorithms operate reliably in real time.
Cross-Functional Leadership: Act as a strategic partner to product managers and engineers, driving the product roadmap through data-driven storytelling and aligning complex algorithmic solutions with overarching business goals.

Professional experience in Data Analytics, Data Science, Applied Research, or algorithmic product optimization, preferably within a dynamic, incentive-driven environment.
Strong analytical mindset with expertise in Machine Learning, behavioral modeling, and causal inference.
Solid foundation in mathematics and economics, including knowledge of elasticity models, incentive response dynamics, and retention analytics.
Advanced SQL and Python proficiency, with experience working with large datasets and the ability to translate research models into production-grade decision logic.
Advanced Experimentation: Experience with A/B testing frameworks, switchback experiments, and evaluating causal effects in networked systems with feedback loops.
Strong communication and stakeholder management skills, with the ability to collaborate closely with engineering teams and influence product decision-making.

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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