Staff Scientist - Earner Science

Staff Scientist - Earner Science Department: Data Science Team: Data Scientist Location: Amsterdam, Netherlands Type: Full-Time ## **About the Team & Role** Our mission is to build the best platform for drivers and couriers. The Earner team owns the product experience for earners and uses data to maintain marketplace reliability, improve efficiency, and personalize experiences that help earners progress and maximize earnings. As a Staff Applied Scientist, you’ll translate ambiguous, complex problems into experiments, models, and productionized solutions that move key metrics at scale. ## **What You’ll Do** - Set the science strategy for personalization, marketplace efficiency, reliability, and experimentation guardrails across the earner experience. - Design, run, and analyze large‑scale experiments and drive standardization of best practices across teams. - Build statistical, optimization, and machine learning models (e.g., pricing/matching, supply positioning, ETA/forecasting, incentives, fraud/anomaly detection) with Engineering partners; establish online/offline evaluation and monitoring. - Define metrics and observability for product and marketplace health; create dashboards, alerts, and automated analyses that detect regressions and quantify causal impact. - Lead multi‑team initiatives from problem framing → modeling/experimentation → decision → production → post‑launch monitoring; provide technical leadership across multiple roadmaps. - Advance causal inference and optimization frameworks to inform product and policy decisions, including counterfactual simulation and sensitivity analysis. - Mentor and uplevel scientists and analysts through design/code reviews, reusable tooling, documentation, and hiring; raise the bar for scientific rigor. - Communicate crisply to leadership audiences via narratives and reviews; influence prioritization and resourcing with data‑driven recommendations. ## **Minimum Qualifications (Must‑Have)** - M.S. or Ph.D. required in Statistics, Economics, Machine Learning, Operations Research, Computer Science, or a related quantitative field. (Ph.D. preferr