Staff Machine Learning Engineer - Driver Incentives

About the position

We are looking for 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, and/or operations research to improve the Uber user experience as well as the overall marketplace performance. You'll be joining the Driver Incentive team to design, evaluate, and build promotional products for drivers. As a Staff machine learning engineer you will solve key business problems including designing incentives structures for drivers, measuring causal impacts of promotions, and creating ML-driven optimizations for marketplace growth. You will work hand in hand with product, engineering, and operations on new product launches, experiment design and algorithm development. We are a fast-moving team and are looking for creative and curious minds to join us!

Responsibilities

• Build statistical, optimization, and machine learning models for applications including pricing, targeting, and experimentation.
• Work with engineers and product managers to turn data science prototypes into robust, reliable machine learning (ML) solutions.
• Use data to understand product performance and to identify improvement opportunities.
• Solve ambiguous, challenging business problems using data-driven approaches.
• Work closely with multi-functional leads to develop technical vision, new methodological approaches, and drive team direction.
• Develop new methodologies for data science including modeling, coding, analytics, optimization, and experimentation.
• Collaborate with cross-functional teams such as product, operations, and marketing to drive system development end-to-end from conceptualization to final product.

Requirements

• Ph.D. or M.S. in Statistics, Economics, Mathematics, Computer Science, Machine Learning, Operations Research, or other quantitative fields.
• 6+ years of industry experience in machine learning, including building and deploying ML models at scale.
• Experience in modern deep learning architectures and probabilistic modeling.
• Proficiency in programming languages (Python, Java, Scala) and ML frameworks (TensorFlow, PyTorch, Scikit-Learn).
• Solid understanding of MLOps practices, including design documentation, testing, and source code management with Git.
• Advanced skills in the development and deployment of large-scale ML models and optimization algorithms.
• Strong business and product sense: ability to shape vague questions into well-defined analyses and success metrics that drive business decisions.

Nice-to-haves

• Expertise in developing causal inference methodologies, experimental designs, and advanced analytical methods.
• Strong experience in building a wide range of models (e.g. causal inference, optimization, ML) for business applications.
• Experience in algorithm development and rapid prototyping.
• Design, develop, and operationalize econometric models to assess challenging causal problems such as product incrementality and long-term value.
• Propose, design, and analyze large scale online experiments and interpret the results to draw actionable conclusions.
• Ability to drive clarity on the best modeling solution for a business objective.
• Collaborate with cross-functional teams across disciplines such as product, engineering, and operations to drive system development end-to-end from generating ideas to productionizing.

Benefits

• Eligible to participate in Uber's bonus program.
• May be offered an equity award & other types of compensation.
• Eligible for various benefits.