AI Data Scientist Team Lead

Location: Work from home (Pennsylvania) Shift: Days (United States of America) Scheduled Weekly Hours: 40 Worker Type: Regular Exemption Status: Yes Job Summary: The AI Data Scientist Team Lead (Manager, AI Platform Engineering) architects end-to-end AI solutions and leads the AI Platform team for Geisinger's AI Department. This is a hands-on technical leadership role, splitting time equally between solution architecture and engineering management (50% technical / 50% leadership). On the technical side, the Team Lead serves as the solution architect across the AI Platform portfolio: gathering requirements from clinical informaticists, data scientists, and business stakeholders; designing production-grade AI architectures spanning batch and real-time workloads; and making build-vs-buy calls for emerging AI capabilities. On the management side, the Team Lead runs the team's rituals, removes blockers, develops direct reports, and manages stakeholder expectations. The AI Platform team is an enabling team—not a delivery team—that builds the reusable capabilities, tooling, and infrastructure that let product teams deploy AI safely and quickly. The team consists of 8 engineers supporting 10 platform capabilities across 70 AI programs. The Team Lead owns the team's capability roadmap, capacity allocation, platform engineering standards, and architecture reviews, while translating organizational AI strategy into executable technical plans that deliver production-grade capabilities across the portfolio. 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 quality, lineage, and access controls across AI data assets  Engineering Management Responsibilities (50% Leadership):  Lead multiple concurrent AI projects; manage scope, timelines, and technical risk while removing obstacles for the team  Mentor and develop 4 direct-report engineers; provide technical leadership and formal performance input for 3 matrixed engineers  Establish platform engineering best practices, conduct architecture reviews, and foster engineering excellence across the full team  Align technical execution with strategic goals; contribute data-driven insights to inform organizational AI initiatives  Coordinate cross-functional collaboration between the AI Platform team and data scientists, software engineers, clinical informaticists, and business stakeholders  Champion scalable and governed AI practices across the organization  Run team rituals (daily standups, weekly planning, architecture office hours, biweekly demos, monthly capability health reviews, quarterly roadmap refresh)  How the Role Operates:  Prioritization : The Team Lead owns the team's roadmap, balancing strategic alignment (capabilities that unblock the highest-value portfolio initiatives), breadth of impact (work that benefits the most programs wins over single-program requests), and operational urgency (production incidents, security issues, governance blockers jump the queue)  Intake : Product teams request platform support through a lightweight intake process the Team Lead manages; requests are triaged weekly—absorbed into the roadmap, handled as quick-turn asks, or redirected to self-serve documentation  Matrix management : For direct reports, owns the full management stack (roadmap, career development, performance, HR). For matrixed engineers, owns the work (roadmap, priorities, platform standards, architecture reviews) and