Senior Manager, AI and Data Science

Key Responsibilities

• Build and operate production AI agents and orchestration workflows using modern agentic frameworks such as LangGraph and deepagents.
• Design modular, reusable components for reasoning, retrieval, tool integration (including MCP), and workflow automation.
• Partner with the Agentic UI team to deliver seamless, end-to-end products.
• Own and optimize the retrieval stack, including vector and hybrid search, embeddings, chunking, and reranking.
• Build evaluation and regression safeguards for LLM outputs, with particular attention to citation faithfulness and groundedness.
• Design and deploy machine learning on real-world evidence (claims, EHR, registry), HEOR, and field-medical data to surface Medical Affairs insights.
• Automate and scale data-ingestion pipelines into AI/ML-ready data on platforms such as Databricks.
• Own deployment, monitoring, and observability (for example, using LangSmith) for the systems you ship.
• Cross-Functional Engagement & Governance: Translate complex AI concepts into clear, actionable insights for technical and non-technical Medical Affairs partners. Align AI initiatives with enterprise governance, privacy, and PHI and compliance expectations.

Qualifications

• A demonstrated record of scientific rigor: either a Ph.D. in a quantitative or medical-adjacent field with first-author publications, or a strong engineering background paired with peer-reviewed research at top venues (for example, NeurIPS, ICML, ICLR, or ML4H).
• 7+ years of applied machine learning or data science, including 3+ years shipping ML or AI systems to production.
• 1+ year of hands-on agentic AI development using modern frameworks (for example, LangGraph, deepagents, or LangSmith).
• Strong RAG and retrieval engineering, including vector and hybrid search, embeddings, and reranking, with the ability to evaluate groundedness and citation accuracy.
• Applied ML on real-world or patient-level data (claims, EHR, registry), including feature engineering.
• Experience with production reliability, data access and authentication, and handling sensitive (PHI) data.
• Strong communication skills and the ability to collaborate across scientific, technical, and business teams.
• Experience in Healthcare or Life Sciences.