Senior Decision Intelligence Engineer (Remote/Hybrid)

Humana Inc; Charleston (WV); Hybrid; USD 107,000 - 147,000; Full time

Job summary

Humana Inc. seeks a Senior Machine Learning Engineer, Decision Intelligence to build, deploy, and operate ML and decisioning pipelines for the NBA Decision Intelligence Platform.

This role focuses on production pipeline development, MLOps, feature engineering, scoring workflows, monitoring, and optimization-aware decisioning to ensure correct member actions while respecting clinical eligibility and channel constraints.

Qualifications

• Bachelors in computer science or relevant field.
• 5+ years of software engineering or quantitative research experience in large-scale production systems.
• 2+ years of software engineering or quantitative research experience in large-scale production systems.
• 2+ years of hands-on experience implementing reinforcement learning or simulation-driven decision systems in production.

Responsibilities

• Build, deploy, and operate ML and decisioning pipelines for the NBA Decision Intelligence Platform.
• Develop production pipelines, MLOps, feature engineering, and scoring workflows.
• Monitor and optimize decision systems while respecting eligibility, constraints, and program goals.
• Collaborate with ML engineers, data engineers, platform engineers, product owners, and decision engine teams to deliver scalable, auditable production systems.

Skills

Python 3.x

Reinforcement learning

Operations research

Distributed systems

Education

Bachelor's degree in Computer Science or related field

Tools

PyTorch

TensorFlow

Ray RLlib

Databricks

PySpark

Delta Lake

MLflow

Job description

Humana Inc. seeks a Senior Machine Learning Engineer, Decision Intelligence to build, deploy, and operate ML and decisioning pipelines for the NBA Decision Intelligence Platform.

This role focuses on production pipeline development, MLOps, feature engineering, scoring workflows, monitoring, and optimization-aware decisioning to ensure correct member actions while respecting clinical eligibility and channel constraints.