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AI Data Scientist Team Lead - Geisinger
Data Scientist
Leads Geisinger's AI Platform team, architecting end-to-end AI solutions for clinical data. Balances hands‑on development with engineering management, translating stakeholder requirements into scalable models and services using Python, deep‑learning frameworks, cloud infrastructure, and container orchestration.
About the role
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Job Summary:
Job Duties:
What You Will Own:
- Solution architecture across all platform capabilities (agentic AI systems, RAG pipelines, multi-model orchestration, real-time and batch ML infrastructure)
- Requirements gathering and technical specification for AI programs across clinical and operational domains
- Build-vs-buy and technology selection decisions for emerging AI capabilities, including generative AI, foundation models, and LLM applications
- Platform engineering standards, architecture reviews, and governance compliance (HIPAA, AI risk management, responsible AI principles)
- Team roadmap, capacity allocation, and intake triage for platform support requests
- People management, career development, and performance evaluation for 4 direct reports (3 MLOps Engineers, 1 Full Stack Engineer)
- Work direction, priorities, platform standards, and formal performance input for 3 matrixed engineers from partner departments (Sr. Platform Data Engineer, Sr. Software Engineer for Integration & Interfaces, Sr. Platform Engineer)
What You Will Not Own:
- Individual capability delivery (delegated to the team via RACI)
- Product strategy or portfolio prioritization (owned by the AI Product Management function)
- Discipline-specific technical standards (set department-wide by the MLOps and Data Science Technical Discipline Leads; set by home-department tech leads for matrixed engineers)
- HR management or final performance evaluations for matrixed engineers (owned by their home departments)
- Day-to-day Databricks workspace administration (owned by the Sr. Platform Data Engineer)
Solution Architecture Responsibilities (50% Technical):
- Design scalable AI architectures spanning batch and real-time workloads, ensuring solutions are production-grade, maintainable, and aligned with organizational priorities
- Gather and refine requirements from clinical informaticists, data scientists, and business stakeholders; translate complex needs into actionable technical specifications
- Architect agentic AI systems, RAG pipelines, and multi-model orchestration frameworks across clinical and operational domains
- Serve as technical authority on end-to-end AI pipeline design across Databricks, cloud-native platforms, and Epic integration points
- Drive build-vs-buy and technology selection decisions for emerging AI capabilities (generative AI, foundation models, LLM applications)
- Ensure AI systems adhere to healthcare security standards (HIPAA), AI governance frameworks, and responsible AI principles
- Partner with data architects and governance teams to enforce data qu