Remote Lead Applied Scientist — Next Best Action AI

Humana, Inc.; Lansing (MI); Ibrido; USD 129.000 - 178.000; Tempo pieno

Descrizione del lavoro

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

You will own models and decision policies end-to-end—from prototyping to deployment and monitoring—collaborating with data scientists, engineers, and business stakeholders. The role requires strong ML expertise, reinforcement learning knowledge, and production engineering experience.

Competenze

• Bachelor’s degree in a quantitative field.
• 6+ years of professional ML/Applied Science experience.
• Experience designing and deploying ML in production.
• Strong understanding of reinforcement learning and sequential decision making.
• Strong Python skills and experience with PyTorch or TensorFlow.
• Experience with distributed data processing platforms (Databricks, PySpark, Ray).
• Solid software engineering fundamentals and production readiness.
• Ability to translate business problems into analytical models and production solutions.
• Experience owning models across full lifecycle: prototyping, offline eval, deployment, monitoring.

Mansioni

• Design, develop, and scale ML models for NBA platform.
• Apply decision science and simulation to optimize outcomes.
• Build production ML pipelines and improve data processing.
• Evaluate model performance and policy behavior pre- and post-deployment.
• Own models and decision policies throughout their lifecycle, collaborating with engineering teams.

Conoscenze

Bachelor’s degree

6+ years ML

Production ML

Reinforcement learning

Python

PyTorch

Distributed data processing

Formazione

BSc/BE in quantitative field

Strumenti

Databricks

PySpark

Ray

MLflow

Descrizione del lavoro

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

You will own models and decision policies end-to-end—from prototyping to deployment and monitoring—collaborating with data scientists, engineers, and business stakeholders. The role requires strong ML expertise, reinforcement learning knowledge, and production engineering experience.