Uber is looking to optimize pricing using optimization, machine learning, and causal inference to foster growth and increase profitability
Requirements
- Strong programming skills e.g. Python and a deep understanding of object-oriented design, data structures, and algorithms
- Solid knowledge of machine learning fundamentals (e.g., model types, feature engineering, evaluation metrics)
- Hands-on experience building and deploying machine learning models in a production environment
- Experience with high-performance backend languages (e.g., Java, Go, C++) for low-latency microservices
- Experience with large-scale data processing and training systems (e.g., Spark, Hive, Presto)
Responsibilities
- Problem Formulation: Partner with Scientists, Product Managers, and stakeholders to translate ambiguous business challenges into concrete ML formulations, system designs, and data requirements
- Build & Deploy: Design, build, and deploy the end-to-end infrastructure and services that power our real-time pricing models at a global scale
- Productionize ML: Own the full production lifecycle of machine learning models. This includes building scalable training/inference pipelines, optimizing models for low-latency serving, and setting up robust monitoring to ensure system health and reliability
- Engineer Data: Develop and maintain the complex feature engineering pipelines that feed our models, working with both batch and near-real-time data sources
Other
- Master's/Bachelor's degree in a similar field with 2+ years of relevant industry experience OR Ph.D. in a quantitative field (e.g., Computer Science, Engineering, Mathematics)
- Excellent communication skills, with the ability to convey complex technical concepts to diverse audiences
- Proven ability to work with Product Managers and partner with Scientists to translate research and business needs into scalable engineering solutions
