Principal Applied Machine Learning Scientist
New
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Omada HealthDigital Health
Remote, USAFull-TimePrincipal
SalaryCalifornia, New York State and Washington State Base Compensation Ranges: $270,480 - $338,100*, Colorado Base Compensation Ranges: $258,720 - $323,400*
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Job Details
- Required Skills
- PythonMachine LearningMLOps
Requirements
- Ph.D. in Computer Science, Statistics, Machine Learning, Biostatistics, Applied Mathematics, or related quantitative field (or Master's with substantial senior-level experience).
- Multiple years of post-secondary research experience in machine learning or applied research science.
- Strong record of delivering novel algorithms or high-impact ML systems in production.
- Deep expertise in time-series or longitudinal modeling, healthcare prediction, or recommender systems.
- Expertise in reinforcement learning or causal inference.
- Strong proficiency in Python and modern ML tooling.
- Experience deploying models into production environments on cloud platforms (e.g., AWS SageMaker).
- Demonstrated ability to translate ambiguous business questions into well-scoped technical problems.
- Ability to communicate tradeoffs clearly to non-technical stakeholders and incorporate feedback into model design.
Responsibilities
- Lead research and development of individual- and population-level health trajectory models using real-world longitudinal healthcare data.
- Produce high-quality experimental evidence and technical recommendations to inform product features and improve health outcomes.
- Design next-best-action algorithms that tailor intervention decisions to a member's specific context and trajectory.
- Research and apply advanced decision and recommendation policies to optimize intervention choices safely within a healthcare environment.
- Define objective functions, reward signals, and policy constraints while partnering with product and clinical teams.
- Serve as the senior scientific lead for algorithmic rigor and publication-quality analysis.
- Mentor scientists and data scientists on advanced temporal modeling, reinforcement learning, and causal inference methods.
- Collaborate with platform, MLOps, and product engineering teams to ensure research outputs are productionized, monitored, and reliable.
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