Principal Applied Machine Learning Scientist

Omada Health

2h•$258,720 - $338,100•Remote

About The Position

Omada Health is looking for a Principal Applied ML Scientist to lead high-impact research and applied algorithm development focused on predicting where a member is headed next and identifying the intervention most likely to improve outcomes at a specific moment. The role sits at the intersection of machine learning research, causal decisioning, and healthcare product impact, translating longitudinal member data into clinically meaningful and operationally deployable algorithms. This position requires deep technical leadership, strong publication-quality rigor, and the ability to work cross-functionally with product, engineering, and clinical stakeholders.

Requirements

Ph.D. in Computer Science, Statistics, Machine Learning, Biostatistics, Applied Mathematics or a related quantitative field is required, will consider a Master’s with substantial, directly related experience at a senior level.
Multiple years of post-secondary education experience in machine learning research or applied research science, with a strong record of delivering novel algorithms or high-impact ML systems in production.
Deep expertise in time-series or longitudinal modeling, healthcare prediction, recommender systems, reinforcement learning, causal inference, or adjacent research areas relevant to trajectories and next-best-action decisioning.
Strong proficiency in Python and modern ML tooling, along with experience deploying models into production environments on cloud platforms such as AWS SageMaker or equivalent.
Demonstrated ability to translate ambiguous business questions into well-scoped technical problems, communicate tradeoffs clearly to non-technical stakeholders, and incorporate feedback into model and metric design.

Nice To Haves

Background in healthcare, digital health, health plans/PBMs, or other complex, regulated industries.
Peer-reviewed papers, conference presentations or white papers in machine learning, reinforcement learning, causal inference or health AI.

Responsibilities

Lead research and development of individual- and population-level health trajectory models that predict future member states, risks, and likely progression paths using messy, real-world longitudinal healthcare data.
Produce high-quality experimental evidence and technical recommendations that can lead to tangible product features and have a real impact on individual and population health trajectories.
Lead the design of next-best-action algorithms that convert predicted trajectories into intervention decisions tailored to a member’s current context and likely future path.
Research and apply advanced decision and recommendation policies to safely optimize intervention choice in a healthcare environment.
Define objective functions, reward signals, and policy constraints that balance engagement, clinical effectiveness, fairness, and operational feasibility, partnering with product and clinical teams to ensure outputs are actionable and interpretable.
Serve as the senior scientific lead for algorithmic and evaluation rigor in trajectories and next-best-action, setting standards for problem formulation, evaluation, and publication-quality analysis.
Mentor other scientists and data scientists on advanced methods in temporal modeling, reinforcement learning, and causal inference.
Collaborate closely with platform, MLOps, and product engineering teams to ensure research outputs can be productionized reliably and monitored appropriately.