Applied Scientist - Marketplace Fairness

Applied Scientist - Marketplace Fairness Department: Data Science Team: Data Scientist Location: New York, United States Type: Full-Time **About the Role** The Marketplace Fairness Team is an applied science policy team that assesses bias in products, projects, and machine learning models. It works closely with legal and policy stakeholders to conduct, present, and interpret these tests. \-\-\-\- What You Will Do ---- 1. Conduct fairness testing to assess products, projects, or machine learning models for bias. 2. Present results to lawyers, leadership, and the people who create these products 3. Help build AI/ML governance \-\-\-\- Basic Qualifications ---- 1. Deeply proficient in doing data analysis in Python 2. Proficient with SQL \-\-\-\- Preferred Qualifications ---- 1. Experience conducting data science in the area of demographic disparities, especially in contexts where causal inference is challenging 2. Experience conducting data science in high-stakes settings, like litigation For New York, NY-based roles: The base salary range for this role is USD$161,000 per year - USD$179,000 per year. For San Francisco, CA-based roles: The base salary range for this role is USD$161,000 per year - USD$179,000 per year. For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link [https://jobs.uber.com/en/benefits](https://jobs.uber.com/en/benefits). 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 fuels progress. What moves us, moves the world - let's move it forward, together. Uber is proud to be an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other charact