Senior Director, Knowledge Management & Retrieval Strategy

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and Med Tech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn

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As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

Job Function:

Data Analytics & Computational Sciences

Job Sub Function:

Data Science

Job Category:

People Leader

All Job Posting Locations:

Cambridge, Massachusetts, United States of America, La Jolla, California, United States of America, Spring House, Pennsylvania, United States of America, Titusville, New Jersey, United States of America

Job Description:

About Innovative Medicine

Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow.

Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way.

Learn more at

Overview

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, 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.

Key Responsibilities

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 sematic 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…