Staff Scientist - Earner Science

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. preferred.)
• 8+ years industry experience as an Applied/Data Scientist (or equivalent), including leading multi-quarter, cross-functional initiatives that shipped to production. (10+ years preferred.)
• Deep expertise in statistical inference, experimental design, causal inference/econometrics, machine learning, optimization, and analytics.
• Proficiency in Python and SQL with production-minded code quality; experience working efficiently with large-scale datasets and distributed tools (e.g., Spark, Hive/Presto; HDFS/data lake/warehouse ecosystems).
• Proven track record designing, running, and interpreting large-scale experiments and synthesizing results into actionable conclusions across multiple KPIs and guardrails.
• Demonstrated ability to influence senior leadership and to communicate complex technical concepts to technical and non-technical stakeholders.

Preferred Qualifications

• Expertise in at least one of: A/B experimentation design, causal inference, ML system design, deep learning for ranking/recommendations, or large-scale optimization.
• Marketplace experience (e.g., pricing, matching, incentives, supply-demand balancing, ETA forecasting) and/or risk/fraud analytics.
• Experience establishing experimentation platforms or practices
• Proficiency with additional languages/frameworks (e.g., Scala/Spark, Java, Go, or R); familiarity with feature stores and online/offline experimentation tooling.
• Track record of mentoring and technical leadership: setting standards, reviewing designs/analyses, and shaping team strategy.

Uber's mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuelds progress. What moves us, moves the world - let's move it forward, together.

Offices continue to be central to collaboration and Uber's cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.

*Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to accommodations@uber.com.

Client-provided location(s): Amsterdam, Netherlands

Job ID: Uber-150696

Employment Type: FULL_TIME

Posted: 2025-11-08T00:37:03