Skip to main content
Omada Health

Principal Applied Machine Learning Scientist

RemoteUnited States only
Published
Role
AI / ML
Experience
Principal
Employment
Full-time
$270.5k–$338.1k/yr
Check eligibility

Open to US only. Set where you work from to check your eligibility.

No BS summary

Principal-level applied ML scientist for healthcare trajectory modeling and next-best-action algorithms. Requires Ph.D. or senior-level equivalent, deep ML research expertise, Python, modern ML tooling, and production cloud deployment experience. Remote USA only.

Core skills

Machine LearningReinforcement LearningCausal Inference

Required skills

PythonAWS SageMaker

What you'll do

  • 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.

What they require

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

Benefits

  • Competitive salary with generous annual cash bonus
  • Equity grants
  • Employee stock purchasing plan (ESPP)
  • Remote first work from home culture
  • Flexible Time Off to help you rest, recharge, and connect with loved ones
  • Generous parental leave
  • Health, dental, and vision insurance (and above market employer contributions)
  • 401k retirement savings plan
  • Lifestyle Spending Account (LSA)
  • Mental Health Support Solutions
  • Certified as a Great Place to Work
HealthcareMid-size

Details

Apply routeGreenhouse
$270.5k–$338.1k/yr