Lead Applied Scientist, NBA – Reinforcement Learning

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Humana is seeking a Lead Applied Scientist to design, develop, and scale intelligent decision systems powering our Next Best Action (NBA) platform.

Our platform combines machine learning, reinforcement learning, and decision intelligence to deliver personalized recommendations that improve healthcare experiences and outcomes for millions of members.

We are looking for a hands-on technical leader who can translate complex business problems into scalable, production-ready decision systems. The ideal candidate combines strong machine learning expertise with an understanding of decision science and software engineering.

This role goes beyond building predictive models. You will have end-to-end ownership of models and decision policies, from initial prototyping and experimentation through offline evaluation, simulation, testing, production deployment, and ongoing monitoring. You will help define how intelligent systems make decisions, evaluate their behavior under real-world constraints and uncertainty, and continuously improve their performance based on production outcomes.

Machine Learning and Reinforcement Learning

Design, develop, and improve machine learning and reinforcement learning models for personalized recommendations, sequential decision making, and policy learning.

Decision Intelligence and Simulation

Apply decision science, operations research, and simulation techniques to formulate complex decision problems, evaluate alternatives, and improve outcomes under business and operational constraints.

Scalable ML Engineering

Develop and improve production ML pipelines using Python, PyTorch, Databricks, PySpark, Ray RLlib, and MLflow, supporting large-scale data processing, model training, and inference.

Model Evaluation and Experimentation

Design experiments, simulations, offline evaluations, and performance metrics to assess model effectiveness, policy behavior, decision quality, and business impact before and after deployment.

Production Deployment and Monitoring

Own models and decision policies throughout their lifecycle, from prototyping and testing through production deployment, monitoring, and continuous improvement. Partner with software engineering and platform teams to ensure production systems are scalable, reliable, and observable.

Constraint-Aware Decision Making

Incorporate clinical eligibility, member preferences, business rules, operational capacity, and other constraints into intelligent decision systems.

Technical Leadership

Provide technical direction on model architecture, algorithm selection, evaluation methods, engineering best practices, and system design while collaborating with data scientists, engineers, and business stakeholders.

Required Qualifications

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.

Strong 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.