Lead Applied Scientist - Remote/Hybrid NBA Solutions

Humana, Inc.; Denver (CO); Hybrid; USD 129,000 - 178,000; Full time

Benefits offered by this job

Bonus incentive plan

Job summary

Humana, Inc. is seeking a Lead Applied Scientist to design, develop, and scale intelligent decision systems powering our Next Best Action platform. You will own models and policies from prototyping to production, collaborating with engineers and stakeholders to drive measurable healthcare outcomes.

The role emphasizes strong ML/RL expertise, decision science, and scalable ML engineering, with remote/hybrid work and occasional in-person meetings. Expect 40-hour weeks and competitive compensation.

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.
• Strong software engineering fundamentals, including software architecture, code quality, automated testing, and scalable system design.
• Demonstrated ability to translate complex business problems into analytical models and production solutions.
• Demonstrated experience owning models across the full lifecycle, including prototyping, offline evaluation, simulation, testing, deployment, and production monitoring.

Responsibilities

• Design, develop, and improve machine learning and reinforcement learning models for personalized recommendations, sequential decision making, and policy learning.
• Apply decision science, operations research, and simulation techniques to formulate complex decision problems, evaluate alternatives, and improve outcomes under business and operational constraints.
• Develop and improve production ML pipelines using Python, PyTorch, Databricks, PySpark, Ray RLlib, and MLflow, supporting large-scale data processing, model training, and inference.
• Design experiments, simulations, offline evaluations, and performance metrics to assess model effectiveness, policy behavior, decision quality, and business impact before and after deployment.
• 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.
• Incorporate clinical eligibility, member preferences, business rules, operational capacity, and other constraints into intelligent decision systems.
• 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.

Skills

Machine learning

Reinforcement learning

Python

PyTorch

TensorFlow

Distributed data processing

Databricks

PySpark

Ray RLlib

MLflow

Education

Bachelor’s degree (quantitative field)

Tools

Databricks

PySpark

Ray RLlib

MLflow

Job description

Humana, Inc. is seeking a Lead Applied Scientist to design, develop, and scale intelligent decision systems powering our Next Best Action platform. You will own models and policies from prototyping to production, collaborating with engineers and stakeholders to drive measurable healthcare outcomes.

The role emphasizes strong ML/RL expertise, decision science, and scalable ML engineering, with remote/hybrid work and occasional in-person meetings. Expect 40-hour weeks and competitive compensation.