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Staff Scientist - Earner Science

Uber

Staff Scientist - Earner Science

Uber logo

Uber

full-time

Posted: November 5, 2025

Number of Vacancies: 1

Job Description

Staff Scientist - Earner Science

đź“‹ Job Overview

As a Staff Scientist at Uber, you will lead the science strategy for the Earner team, focusing on personalization, marketplace efficiency, and reliability. You will design and analyze large-scale experiments, build advanced models, and lead multi-team initiatives to enhance the earner experience. This role requires deep technical expertise and the ability to influence senior leadership with data-driven insights.

📍 Location: Amsterdam, Netherlands

🏢 Department: Data Science

đź“„ Full Description

## **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](mailto:accommodations@uber.com).

🎯 Key Responsibilities

  • Set the science strategy for personalization, marketplace efficiency, reliability, and experimentation guardrails.
  • Design, run, and analyze large-scale experiments and drive standardization of best practices.
  • Build statistical, optimization, and machine learning models with Engineering partners; establish evaluation and monitoring.
  • Define metrics and observability for product and marketplace health; create dashboards, alerts, and automated analyses.
  • Lead multi-team initiatives from problem framing to post-launch monitoring; provide technical leadership.
  • Advance causal inference and optimization frameworks to inform product and policy decisions.
  • Mentor and uplevel scientists and analysts; raise the bar for scientific rigor.
  • Communicate to leadership audiences; influence prioritization and resourcing with data-driven recommendations.

âś… Required Qualifications

  • M.S. or Ph.D. in Statistics, Economics, Machine Learning, Operations Research, Computer Science, or a related quantitative field.
  • 8+ years industry experience as an Applied/Data Scientist, including leading multi-quarter, cross-functional initiatives that shipped to production.
  • 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.
  • Proven track record designing, running, and interpreting large-scale experiments and synthesizing results into actionable conclusions.
  • Demonstrated ability to influence senior leadership and to communicate complex technical concepts to technical and non-technical stakeholders.

🛠️ Required Skills

  • Statistical inference
  • Experimental design
  • Causal inference/econometrics
  • Machine learning
  • Optimization
  • Analytics
  • Python
  • SQL
  • Large-scale dataset management
  • Distributed tools (e.g., Spark, Hive/Presto)

Locations

  • Amsterdam, Netherlands

Salary

Estimated Salary Rangemedium confidence

120,000 - 180,000 EUR / yearly

Source: ai estimated

* This is an estimated range based on market data and may vary based on experience and qualifications.

Skills Required

  • Statistical inferenceintermediate
  • Experimental designintermediate
  • Causal inference/econometricsintermediate
  • Machine learningintermediate
  • Optimizationintermediate
  • Analyticsintermediate
  • Pythonintermediate
  • SQLintermediate
  • Large-scale dataset managementintermediate
  • Distributed tools (e.g., Spark, Hive/Presto)intermediate

Required Qualifications

  • M.S. or Ph.D. in Statistics, Economics, Machine Learning, Operations Research, Computer Science, or a related quantitative field. (experience)
  • 8+ years industry experience as an Applied/Data Scientist, including leading multi-quarter, cross-functional initiatives that shipped to production. (experience)
  • Deep expertise in statistical inference, experimental design, causal inference/econometrics, machine learning, optimization, and analytics. (experience)
  • Proficiency in Python and SQL with production-minded code quality; experience working efficiently with large-scale datasets and distributed tools. (experience)
  • Proven track record designing, running, and interpreting large-scale experiments and synthesizing results into actionable conclusions. (experience)
  • Demonstrated ability to influence senior leadership and to communicate complex technical concepts to technical and non-technical stakeholders. (experience)

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. (experience)
  • Marketplace experience and/or risk/fraud analytics. (experience)
  • Experience establishing experimentation platforms or practices. (experience)
  • Proficiency with additional languages/frameworks; familiarity with feature stores and online/offline experimentation tooling. (experience)
  • Track record of mentoring and technical leadership: setting standards, reviewing designs/analyses, and shaping team strategy. (experience)

Responsibilities

  • Set the science strategy for personalization, marketplace efficiency, reliability, and experimentation guardrails.
  • Design, run, and analyze large-scale experiments and drive standardization of best practices.
  • Build statistical, optimization, and machine learning models with Engineering partners; establish evaluation and monitoring.
  • Define metrics and observability for product and marketplace health; create dashboards, alerts, and automated analyses.
  • Lead multi-team initiatives from problem framing to post-launch monitoring; provide technical leadership.
  • Advance causal inference and optimization frameworks to inform product and policy decisions.
  • Mentor and uplevel scientists and analysts; raise the bar for scientific rigor.
  • Communicate to leadership audiences; influence prioritization and resourcing with data-driven recommendations.

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Uber logo

Staff Scientist - Earner Science

Uber

Staff Scientist - Earner Science

Uber logo

Uber

full-time

Posted: November 5, 2025

Number of Vacancies: 1

Job Description

Staff Scientist - Earner Science

đź“‹ Job Overview

As a Staff Scientist at Uber, you will lead the science strategy for the Earner team, focusing on personalization, marketplace efficiency, and reliability. You will design and analyze large-scale experiments, build advanced models, and lead multi-team initiatives to enhance the earner experience. This role requires deep technical expertise and the ability to influence senior leadership with data-driven insights.

📍 Location: Amsterdam, Netherlands

🏢 Department: Data Science

đź“„ Full Description

## **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](mailto:accommodations@uber.com).

🎯 Key Responsibilities

  • Set the science strategy for personalization, marketplace efficiency, reliability, and experimentation guardrails.
  • Design, run, and analyze large-scale experiments and drive standardization of best practices.
  • Build statistical, optimization, and machine learning models with Engineering partners; establish evaluation and monitoring.
  • Define metrics and observability for product and marketplace health; create dashboards, alerts, and automated analyses.
  • Lead multi-team initiatives from problem framing to post-launch monitoring; provide technical leadership.
  • Advance causal inference and optimization frameworks to inform product and policy decisions.
  • Mentor and uplevel scientists and analysts; raise the bar for scientific rigor.
  • Communicate to leadership audiences; influence prioritization and resourcing with data-driven recommendations.

âś… Required Qualifications

  • M.S. or Ph.D. in Statistics, Economics, Machine Learning, Operations Research, Computer Science, or a related quantitative field.
  • 8+ years industry experience as an Applied/Data Scientist, including leading multi-quarter, cross-functional initiatives that shipped to production.
  • 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.
  • Proven track record designing, running, and interpreting large-scale experiments and synthesizing results into actionable conclusions.
  • Demonstrated ability to influence senior leadership and to communicate complex technical concepts to technical and non-technical stakeholders.

🛠️ Required Skills

  • Statistical inference
  • Experimental design
  • Causal inference/econometrics
  • Machine learning
  • Optimization
  • Analytics
  • Python
  • SQL
  • Large-scale dataset management
  • Distributed tools (e.g., Spark, Hive/Presto)

Locations

  • Amsterdam, Netherlands

Salary

Estimated Salary Rangemedium confidence

120,000 - 180,000 EUR / yearly

Source: ai estimated

* This is an estimated range based on market data and may vary based on experience and qualifications.

Skills Required

  • Statistical inferenceintermediate
  • Experimental designintermediate
  • Causal inference/econometricsintermediate
  • Machine learningintermediate
  • Optimizationintermediate
  • Analyticsintermediate
  • Pythonintermediate
  • SQLintermediate
  • Large-scale dataset managementintermediate
  • Distributed tools (e.g., Spark, Hive/Presto)intermediate

Required Qualifications

  • M.S. or Ph.D. in Statistics, Economics, Machine Learning, Operations Research, Computer Science, or a related quantitative field. (experience)
  • 8+ years industry experience as an Applied/Data Scientist, including leading multi-quarter, cross-functional initiatives that shipped to production. (experience)
  • Deep expertise in statistical inference, experimental design, causal inference/econometrics, machine learning, optimization, and analytics. (experience)
  • Proficiency in Python and SQL with production-minded code quality; experience working efficiently with large-scale datasets and distributed tools. (experience)
  • Proven track record designing, running, and interpreting large-scale experiments and synthesizing results into actionable conclusions. (experience)
  • Demonstrated ability to influence senior leadership and to communicate complex technical concepts to technical and non-technical stakeholders. (experience)

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. (experience)
  • Marketplace experience and/or risk/fraud analytics. (experience)
  • Experience establishing experimentation platforms or practices. (experience)
  • Proficiency with additional languages/frameworks; familiarity with feature stores and online/offline experimentation tooling. (experience)
  • Track record of mentoring and technical leadership: setting standards, reviewing designs/analyses, and shaping team strategy. (experience)

Responsibilities

  • Set the science strategy for personalization, marketplace efficiency, reliability, and experimentation guardrails.
  • Design, run, and analyze large-scale experiments and drive standardization of best practices.
  • Build statistical, optimization, and machine learning models with Engineering partners; establish evaluation and monitoring.
  • Define metrics and observability for product and marketplace health; create dashboards, alerts, and automated analyses.
  • Lead multi-team initiatives from problem framing to post-launch monitoring; provide technical leadership.
  • Advance causal inference and optimization frameworks to inform product and policy decisions.
  • Mentor and uplevel scientists and analysts; raise the bar for scientific rigor.
  • Communicate to leadership audiences; influence prioritization and resourcing with data-driven recommendations.

Target Your Resume for "Staff Scientist - Earner Science" , Uber

Get personalized recommendations to optimize your resume specifically for Staff Scientist - Earner Science. Takes only 15 seconds!

AI-powered keyword optimization
Skills matching & gap analysis
Experience alignment suggestions

Check Your ATS Score for "Staff Scientist - Earner Science" , Uber

Find out how well your resume matches this job's requirements. Get comprehensive analysis including ATS compatibility, keyword matching, skill gaps, and personalized recommendations.

ATS compatibility check
Keyword optimization analysis
Skill matching & gap identification
Format & readability score

Tags & Categories

UberAmsterdamNetherlandsData ScienceData Science

Related Jobs You May Like

No related jobs found at the moment.