Own the complete lifecycle of algorithmic solutions from problem formulation, data exploration, and feature engineering to deployment, monitoring, and iteration
Prioritize and lead deep dives into our data to uncover new product and business opportunities
Partner closely with Engineering to build and scale production-grade ML systems, real-time inference services, batch pipelines, and feature stores
Design, implement, and analyze different types of experiments, and facilitate and foster data-driven and informed decision making and prioritization
Drive scientific excellence by introducing modern techniques in ML, optimization, reinforcement learning, or graph-based methods to unlock new product capabilities
Establish metrics that measure the health of our products, as well as rider and driver experience
Translate complex business challenges into concrete algorithmic solutions in close collaboration with Product, Engineering, Operations, and Science teams
Advanced degree in a quantitative field such as statistics, physics, economics, operations research, neuroscience, or engineering, or relevant work experience
3+ years hands-on experience in a data science or machine learning role working with production machine learning models and optimization systems
Passion for solving unstructured and non-standard mathematical problems
Experience independently driving multi-project algorithmic scopes and navigating technical ambiguity from ideation to delivery
Experience with machine learning models in production, making practical tradeoffs among algorithm sophistication, compute complexity, maintainability, and extensibility in production environments
Strong oral and written communication skills, and ability to collaborate with and influence cross-functional partners
Working knowledge of modern machine learning frameworks and distributed computing systems, including PyTorch, TensorFlow, Ray, Spark, etc