Lead Applied Scientist, NBA, Reinforcement Learning

What you'll do

• Design and develop machine learning and reinforcement learning models for personalized recommendations and sequential decision making.
• Apply decision science and simulation techniques to formulate complex problems and evaluate alternatives under operational constraints.
• Build production ML pipelines using Python, PyTorch, Databricks, PySpark, Ray RLlib, and MLflow.
• Design experiments, simulations, and offline evaluations to assess model behavior and business impact before deployment.
• Own the full model lifecycle from initial prototyping and testing through production deployment and continuous monitoring.
• Incorporate clinical eligibility, member preferences, and business rules into intelligent decision systems.
• Provide technical direction on model architecture, algorithm selection, and engineering best practices to stakeholders.

What we're looking for

• Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, Operations Research, or a related quantitative field.
• 6+ years of professional experience in machine learning, applied science, decision science, or related engineering disciplines.
• Experience designing, developing, testing, deploying, and monitoring machine learning solutions in production environments.
• Solid understanding of reinforcement learning, sequential decision making, or other advanced decision-making techniques.
• Strong Python programming skills and experience with machine learning frameworks such as PyTorch or TensorFlow.
• Experience with distributed data processing and ML platforms such as Databricks, PySpark, Ray, or equivalent technologies.
• Strong software engineering fundamentals, including software architecture, code quality, automated testing, and scalable system design.
• Demonstrated experience owning models across the full lifecycle, including prototyping, offline evaluation, simulation, testing, deployment, and production monitoring.