Staff Data Scientist for Epidemiology Patient ML
Opportunity Summary
You will join a team building an AI-enabled computational platform that supports drug discovery and development using real-world patient and biological data. The work focuses on developing machine learning tools for patient data and enabling their adoption across scientific teams. You will collaborate under the guidance of epidemiology and biology program leads, translating domain requirements into modeling approaches, and helping teams use outputs effectively. The ideal candidate blends strong data science with scientific reasoning, and can create reusable frameworks that make complex modeling accessible to non-ML collaborators.
Ideal Candidate
A staff-level data scientist experienced with epidemiology or patient data ML, who can deliver high-quality models and also turn them into tools teams can adopt. You communicate clearly across scientific and engineering boundaries and build robust validation pipelines.
Project Insights
- Frameworks and tools that reduce scientific friction are key in cross-disciplinary teams
- Validation and bias checks matter heavily when patient data drives decisions
- Close collaboration with domain leads speeds alignment on success criteria
How to Prepare
- Bring one end-to-end example from patient or epidemiology data to validated model outputs
- Describe how you handle missingness, confounding, and dataset shift in real-world data
- Share how you structure documentation and tooling to support adoption by scientists
Typical Challenges
- Modeling from heterogeneous patient and real-world data sources
- Ensuring outputs are interpretable and usable by domain scientists
- Building adoption pathways so teams trust and operationalize the tools
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Project Details
Skills & Requirements
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