Job DescriptionApplied Scientists at Uber use data to improve and automate all aspects of Uber's core rideshare and delivery products. You will be joining the Earner team, which owns our product experience for drivers and couriers. You will work on designing algorithms, building models and implementing products and policies that maintain reliability and improve the efficiency of Uber’s 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 machine learning, statistics, economics, operations research to improve the Uber earner experience as well as the overall marketplace performance. About The RoleThe Vehicles and Sustainability tech team is hiring a Senior Applied Scientist with a passion for solving new and difficult problems with data! Together we will be building the technologies and leading the product development for both Uber mobility and delivery sustainability. You'll be responsible for defining problems, designing models, implementing, coding and deploying models to Uber production systems and ensuring product success. Sitting in the driver seat to lead multi-functional partnership with engineering, product management, operations, business development and policy to get results. You will be the primary person working with other applied scientists and data scientists to help Uber meet its sustainability goals. What You'll Do

• Design, build, deploy machine learning, statistical, optimization models into Uber production systems for a wide range of applications.
• Collaborate with multi-functional teams across areas such as product, engineering, operations, and marketing to drive system development end-to-end from conceptualisation to productionization.
• Drive technical collaboration and alignment with core marketplace science and engineering teams such as pricing, matching, ranking, maps on relevant product and tech levers.
• Understanding product performance and to find opportunities within data.
• Present findings to senior management to influence business decisions.

Basic Qualifications

• Ph.D., M.S., or Bachelors degree in Computer Science, Machine Learning, Statistics, Economics, Operations Research, or other quantitative fields.
• 3+ years of proven experience as an Applied Scientist, Machine Learning Engineer, Research Scientist, Software Engineer or equivalent.
• Experience in production coding and deploying ML, statistical, optimization models in real-time systems.
• Knowledge of underlying mathematical foundations of statistics, machine learning, optimization, economics, and analytics.
• Ability to use Python or other programming languages to work efficiently at scale with large data sets in production systems.
• Proficiency in SQL.

Preferred Qualifications

• 5+ years of experience in the tech industry working on ML, AI or other production systems.
• Designing and developing algorithms to productionizing and deploying models for real-time systems.
• Thought leadership to drive multi-functional projects from conceptualisation to productionization.
• Well-honed communication and presentation skills.

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.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.