Senior Director, Knowledge Management & Retrieval Strategy

Senior Leader, R&D Knowledge Management

We are seeking a senior leader to define and scale R&D knowledge and retrieval capabilities that power GenAI experiences across R&D.

Reporting to the VP, Data Strategy and Products, this role will lead the Knowledge Management team, spanning Knowledge Graph design and engineering, Ontology design and engineering, and semantic layer definition to enable governed, reusable, and AI-ready enterprise knowledge.

This role will operate at the intersection of AI/ML, data products, data platforms, and scientific workflows, ensuring that GenAI systems can reliably retrieve, reason over, and synthesize information from diverse biomedical and operational data sources.

In close partnership with the broader DS&P and JJT (J&J Technology / IT), the leader will define retrieval architectures for agentic AI systems and user-facing applications, harmonize data interfaces and ensure scalable, governed access to R&D knowledge assets.

The role requires both technical depth and organizational navigation, as it bridges GenAI product teams, data strategy and products owners, and scientific stakeholders.

• Knowledge Management Leadership

  • Lead and grow a multidisciplinary Knowledge Management organization, including Knowledge Graph engineers, Ontology engineers/designers, and semantic layer practitioners; set vision, priorities, and ways of working.

  • Own the roadmap for R&D knowledge representation (knowledge graph modeling patterns, ontology strategy, semantic layer standards) aligned to GenAI and R&D outcomes.

  • Establish the language, identity, and semantic consistency of enterprise assets and processes so systems, people, analytics, and AI can operate from shared truth.

  • Educate and promote use of standard taxonomies and identifiers across the organization.

  • Establish operating mechanisms for quality, reuse, and stewardship of semantic assets (definitions, taxonomies/ontologies, entity models, metadata) across domains.

  • Partner with platform, governance, and product teams to ensure semantic assets are discoverable, versioned, and governed and can be consumed through retrieval pipelines and APIs.

• GenAI Retrieval Architecture

  • Shape AI-ready data through integration, semantics, and reusable data products—grounded in real R&D use cases and outcomes

  • Define retrieval strategies supporting agentic AI systems and user-facing GenAI applications.

  • Design approaches for semantic retrieval, knowledge grounding, and contextual data integration across diverse R&D datasets.

  • Guide the development of retrieval pipelines that enable reliable question answering, reasoning, and scientific insight generation.

  • Establish best practices for RAG (Retrieval-Augmented Generation) architectures in regulated scientific environments.

• Data Interface & Platform Alignment

  • In close partnership with JJT (J&J Technology / IT), harmonize and unify access to enterprise data assets.

  • Partner with DS&P Data Products & Governance team on defining standards for data interfaces, and retrieval APIs that support GenAI applications.

  • Ensure alignment between GenAI product requirements and enterprise data infrastructure strategy.

  • Act as a bridge between R&D data users, GenAI product teams, and platform owners.

• Knowledge Systems & Data Strategy

  • Partner with DSH & JJT (J&J Technology / IT) on Identifying and curating high-value knowledge sources across discovery, development, clinical, and regulatory domains.

  • Partner with DS&P Data Products & Governance team & JJT to define data strategies for integrating structured data, documents, knowledge graphs, and scientific literature.

  • Establish principles for data provenance, traceability, and governance in AI-assisted scientific workflows.

• Cross-Functional Collaboration

  • Work closely with:

    • GenAI product teams

    • R&D Data Science groups

    • JJIT platform and architecture teams

    • Scientific domain experts

    • Partners in R&D TAs and functions

  • Translate data needs of complex scientific workflows into data access and retrieval architectures.

• Governance & Quality

  • Ensure that retrieval systems meet standards for:

    • reproducibility

    • traceability

    • auditability

    • responsible AI use in regulated environments.

• Required

  • Formal…