Principal Applied Machine Learning Scientist - Omada Health

About the Role

Omada Health is seeking a Principal Applied ML Scientist to lead high-impact research and algorithm development. You will focus on predicting member health trajectories and identifying optimal interventions at the right moment. This role sits at the intersection of ML research, causal decisioning, and healthcare product impact.

Key Responsibilities

Health Trajectory Research

• Lead the development of individual- and population-level health trajectory models using longitudinal healthcare data.
• Produce high-quality experimental evidence to drive tangible product features.

Next Best Action Algorithms

• Design algorithms that convert predicted trajectories into personalized intervention decisions.
• Research and apply advanced decision and recommendation policies to optimize interventions in a healthcare context.
• Define objective functions and reward signals that balance clinical effectiveness, fairness, and operational feasibility.

Technical Leadership

• Set standards for algorithmic rigor, problem formulation, and evaluation.
• Mentor scientists on temporal modeling, reinforcement learning, and causal inference.
• Collaborate with platform, MLOps, and product engineering teams to ensure reliable production deployment.

Qualifications

• Ph.D. in CS, Statistics, ML, Biostatistics, Applied Math, or related field (Master’s with significant senior-level experience considered).
• Multiple years of post-secondary experience in ML research or applied science with a record of production-level impact.
• Deep expertise in time-series modeling, healthcare prediction, recommender systems, reinforcement learning, or causal inference.
• Strong proficiency in Python and modern ML tooling (e.g., AWS SageMaker).
• Ability to translate ambiguous business questions into scoped technical problems.

Bonus Points

• Background in healthcare, digital health, or regulated industries.
• Record of peer-reviewed papers, conference presentations, or white papers.