\\\\About the Role\\\\ Applied Scientists at Uber use data to improve and automate all aspects of Uber's core rideshare and delivery products. You will be joining the Trip Pricing team, which owns our automated real-time pricing algorithms and platform. You will work on designing and implementing pricing models that maintain reliability and improve the efficiency of Uber's Mobility marketplace. We are looking for experienced candidates with a passion for solving new and difficult problems with data. In this role, you will be able to use your strong quantitative skills in the fields of economics, machine learning, and/or operations research to improve the Uber rider experience as well as the overall marketplace performance. \\\\What the Candidate Will Do\\\\ 1\\. Build statistical, optimization, and machine learning models for a range of applications in the pricing algorithms space. 2\\. Design and execute product experiments and interpret the results to draw detailed and actionable conclusions. 3\\. Use data to understand product performance and to identify improvement opportunities. 4\\. Present findings to senior management to inform business decisions. 5\\. Collaborate with cross-functional teams across disciplines such as product, engineering, operations, and marketing to drive system development end-to-end from ideation to productionization. \\\\Basic Qualifications\\\\ 1\\. Ph.D., M.S., or Bachelors degree in Statistics, Economics, Machine Learning, Operations Research, or other quantitative fields. 2\\. 3+ years of industry experience as an Applied or Data Scientist or equivalent. 3\\. Knowledge of underlying mathematical foundations of statistics, machine learning, optimization, economics, and analytics. 4\\. Experience in experimental design and analysis. 5\\. Experience with exploratory data analysis, statistical analysis and testing, and model development. 6\\. Ability to use Python to work efficiently at scale with large data sets. 7\\. Proficiency in SQL. \\\\Preferred Qualifications\\\\ 1\\. 6+ years of industry experience. 2\\. Experience in algorithm development and prototyping. 3\\. Experience in pricing optimization. 4\\. Experience with productionizing algorithms for real-time systems. 5\\. Well-honed communication and presentation skills. For New York, NY-based roles: The base salary range for this role is USD$216,000 per year - USD$240,000 per year. For San Francisco, CA-based roles: The base salary range for this role is USD$216,000 per year - USD$240,000 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD$216,000 per year - USD$240,000 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 Uber is proud to be an Equal Opportunity/Affirmative Action 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 characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form-

Candidates require a Ph.D., M.S., or Bachelor's degree in a quantitative field such as Statistics, Economics, Machine Learning, or Operations Research. A minimum of three years of industry experience as an Applied or Data Scientist is necessary. Proficiency in the mathematical foundations of statistics, machine learning, optimization, economics, and analytics is essential. The role also requires experience in experimental design and analysis, exploratory data analysis, statistical analysis and testing, and model development. The ability to use Python for large datasets and proficiency in SQL are also required.