Senior Applied Scientist [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.

Key Responsibilities

• 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

Skills, Knowledge and Expertise

• 3+ years of 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

• Professional working proficiency in English

• Nice to have: Experience formulating budget allocation tasks as linear, integer, or mixed-integer optimization problems to find the optimal balance between costs and desired outcomes

• Nice to have: Experience with Uplift Modeling — Proven ability to evaluate causal effects, build propensity/response models, and design targeted interventions