Senior Applied Researcher — Incentives

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.