Applied Scientist, Economist - Marketplace Fairness

Uber

Hybrid

Quick summary

Work type: Hybrid
Location: New York, NYSan Francisco, CA
Salary: $161,000–$161,000 / yr
Posted: 67 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $175k

This role $161k

$116k most similar roles pay here $237k

This role pays less than 69% of similar roles. Most pay $134,250–$214,975 — the shaded band above. At the midpoint, this role pays about $161k versus about $175k for comparable roles.

Based on 240 similar postings.

Employer

About Uber

Uber Technologies, Inc. is the world’s largest, San Francisco-based mobile technology platform facilitating on-demand ride-hailing, food delivery (Uber Eats), and freight transportation across approximately 70 countries.

Uber currently has 329 open roles on FindRole.

Listed pay typically runs $202,000–$202,000 across 71 roles with salary data.

Most-posted roles

View all roles at Uber

At a glance

TL;DR · Applied Scientist, Economist - Marketplace Fairness

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The Applied Scientist, Economist role at the Marketplace Fairness Team involves conducting in-depth investigations and fairness testing to assess potential biases in products and machine learning models. This team works closely with product, data science, legal, and policy teams to ensure compliance with Responsible AI principles. Day-to-day responsibilities include presenting findings to various stakeholders and contributing to AI/ML governance initiatives. The ideal candidate should have a PhD in Economics and be proficient in Python and SQL for large-scale data analysis. Additionally, strong communication skills are essential for translating technical insights into actionable recommendations for non-technical audiences, while project management experience is crucial for navigating cross-functional collaborations within the organization.

Skills

Python SQL R CI/CD

What you'll do

Conduct deep investigations into platform products and projects.
Assess bias in machine learning models using fairness testing methods.
Translate policy concerns into quantitative analysis for legal teams.
Present technical findings to leadership and product development teams.
Build AI/ML governance frameworks to ensure responsible practices.

What we're looking for

PhD in Economics or related quantitative field.
Proficient in Python and SQL for data analysis.
Strong project management skills with cross-functional collaboration experience.
Ability to translate technical findings into clear insights for non-technical audiences.
Experience conducting fairness testing on AI/ML models and products.
Passionate about improving platform equity and user experiences.