AI Data Scientist Team Lead

​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 (3MLOpsEngineers, 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 theMLOpsand 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 intakeprocessthe 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 provides formal input on performance reviews; the engineer's home department owns HR management and final evaluation

• Escalation path:Engineer-level issues resolved directly between engineers; priority conflicts, scope disagreements, and technical decisions with broad impact come to the Team Lead; strategic trade-offs and cross-department conflicts escalate to the VP

Work is typically performed in an office or remote environment. Accountable for satisfying all job specific obligations and complying with all organization policies and procedures. The specific statements in this profile are not intended to be all-inclusive. They represent typical elements considered necessary to successfully perform the job.

*Relevant experience may be a combination of related work experience and degree obtained (Master's Degree = 2 years; PHD = 4 years ).

Key Technologies:

• Databricks (Delta Lake, Unity Catalog,MLflow, Mosaic AI, Spark)

• AWS (ECS/Fargate, Bedrock, S3, IAM), Terraform

• Claude / Amazon Bedrock,LangChain, agentic AI frameworks

• Epic APIs (FHIR, SDE)

• Docker, CI/CD pipelines,MLOpstooling

• Real-time streaming (Kafka, Spark Structured Streaming)

Collaboration Points:

• All AI Platform team roles:direct manager, solution reviewer, escalation point

• Clinical informaticists and data scientists:requirements gathering and solution design

• AI Product Management:roadmap alignment and portfolio prioritization

• AI Department Technical Discipline Leads (MLOps, Data Science):alignment on discipline-specific standards applied to platform work

• AI Governance:compliance with risk frameworks, responsible AI principles, and model risk management

• Enterprise architecture and security:alignment of AI Platform infrastructure with organizational standards

• Partner department managers (IT Platform, IT Software, CDIO Data Management):matrix coordination for matrixed engineers

Required Skills & Qualifications:

• 8+ years in data science, ML engineering, or AI solution architecture, with at least 3 years in a technical leadership or engineering management role

• Demonstrated experience designing production ML/AI systems end-to-end: from data ingestion through model serving and monitoring

• Strong fluency in Python and SQL; hands-on experience with Databricks (MLflow, Unity Catalog, Spark) and cloud-native ML infrastructure (AWS preferred)

• Experience architecting agentic AI systems, LLM applications, or RAG pipelines in production settings

• Proven ability to translate ambiguous business problems into technical specifications and actionable engineering plans

• Track recordof mentoring engineers across multiple specialties and managing concurrent technical projects

• Familiarity with healthcare data standards (HL7/FHIR) and regulatory requirements (HIPAA) strongly preferred

• Experience with Epic integration points (FHIR, SDE) a plus

• MS or PhD in Computer Science, Data Science, or related quantitative field preferred; equivalent experience accepted