About the job
The **Marketplace Signals** team at Uber is responsible for building and optimizing foundational marketplace signals that power user experiences and drive marketplace efficiency. Our team ensures that key signals—such as eyeball ETA, spinner time, and supply reliability indicators—are leveraged effectively across various Uber products and levers, enabling data-driven decision-making and seamless coordination across different business functions.
Responsibilities
- Develop and optimize ML models to enhance key marketplace signals (e.g., ETA predictions, supply availability metrics, demand forecasts).
- Collaborate with cross-functional teams (Pricing, Matching, Driver Incentives, etc.) to ensure marketplace signals are effectively utilized.
- Improve operational efficiency by building a centralized, scalable system for marketplace signals that serves multiple use cases.
- Ensure consistency and reliability across Uber's platform by maintaining high-quality marketplace signals that inform rider and driver experiences.
- Reduce technical debt by streamlining signal infrastructure and minimizing redundant computations.
- Leverage cutting-edge ML techniques (deep learning, probabilistic modeling, reinforcement learning, etc.) to continuously refine marketplace signals.
- Work with real-time streaming data and large-scale distributed systems to ensure Uber's signals are up-to-date and responsive to market dynamics.
Qualifications
Minimum
- 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.
Preferred
- 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.