Principal Applied Machine Learning Scientist, Omada Health

• This position requires deep technical leadership, strong publication-quality rigor, and the ability to work cross-functionally with product, engineering, and clinical stakeholders
• 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

Desirable

• 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
• 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