Lead Applied Scientist, NBA – Reinforcement Learning
Core
Design, develop, and scale intelligent decision systems and reinforcement learning models for personalized healthcare recommendations.
Role type
Lead Applied Scientist (Reinforcement Learning & Decision Intelligence)
Builds
Next Best Action (NBA) platform for personalized healthcare recommendations
Domain
Healthcare / Insurance / Reinforcement Learning
Deliverable
production ML models
Required skills
Reinforcement learning, Sequential decision making, Python, PyTorch, Distributed data processing, ML lifecycle ownership, Software architecture
Preferred skills
Operations research, Simulation, Contextual bandits, Markov Decision Processes, Event-driven architectures, Healthcare domain experience
Technologies
PyTorch, TensorFlow, Databricks, PySpark, Ray RLlib, MLflow, Kafka, Redis
Responsibilities
Design and develop RL models for sequential decision making; Apply decision science and simulation to formulate complex problems; Develop production ML pipelines; Design experiments and offline evaluations; Own models from prototyping to production monitoring; Incorporate clinical and business constraints into decision systems; Provide technical direction on architecture and algorithms.
