Senior Director, Knowledge Management & Retrieval Strategy
Johnson & Johnson
8 days ago
Remote friendly (San Diego, CA)
United States
IT
Overview
- Define and scale R&D knowledge and retrieval capabilities that power GenAI experiences across R&D.
- Lead Knowledge Management spanning Knowledge Graph design/engineering, Ontology design/engineering, and semantic layer definition for governed, reusable, AI-ready enterprise knowledge.
Key Responsibilities
- Lead and grow a multidisciplinary Knowledge Management team; set vision, priorities, and ways of working.
- Own the roadmap for R&D knowledge representation (knowledge graph modeling patterns, ontology strategy, semantic layer standards).
- Establish shared language/identity/semantic consistency for enterprise assets and processes.
- Promote standard taxonomies and identifiers; ensure semantic asset quality, reuse, and stewardship.
- Partner with platform, governance, and product teams so semantic assets are discoverable, versioned, governed, and consumable via retrieval pipelines and APIs.
- Shape AI-ready data via integration/semantics/reusable data products grounded in real R&D use cases.
- Define retrieval strategies for agentic AI and user-facing GenAI apps; guide retrieval pipelines for QA/reasoning/insight.
- Establish best practices for RAG in regulated scientific environments.
- Harmonize access to enterprise data; define standards for data interfaces and retrieval APIs.
- Define data provenance/traceability/governance principles in AI-assisted scientific workflows.
Qualifications
Required
- Training/experience in life sciences, biomedical research, or pharmaceutical R&D.
- Experience leading and developing technical teams (hiring, coaching, performance expectations).
- Success leading cross-functional programs aligning product/platform/governance stakeholders.
- Advanced degree in CS, data science, computational biology, bioinformatics, or related field.
- Deep experience designing data architectures/knowledge systems for AI/ML; strong IR/semantic search/RAG expertise.
- Experience with large-scale enterprise data platforms; ability to collaborate across technical/scientific/platform teams.
Preferred
- Familiarity with omics, clinical data, literature, and regulatory documents.
- Knowledge graph/R&D knowledge system experience.
- Exposure to GenAI platforms, agentic systems, and MCPs; ontology engineering and semantic modeling; semantic layer operationalization.
Benefits (time off)
- Vacation 120 hours/year; Sick time 40 hours/year (48 in CO; 56 in WA); Holiday pay 13 days/year; Work/Personal/Family up to 40 hours/year; Parental Leave 480 hours; Bereavement Leave 240 hours (immediate family) / 40 hours (extended family); Caregiver Leave 80 hours (52-week rolling) / 10 days; Volunteer Leave 32 hours/year; Military Spouse Time-Off 80 hours/year.
- Define and scale R&D knowledge and retrieval capabilities that power GenAI experiences across R&D.
- Lead Knowledge Management spanning Knowledge Graph design/engineering, Ontology design/engineering, and semantic layer definition for governed, reusable, AI-ready enterprise knowledge.
Key Responsibilities
- Lead and grow a multidisciplinary Knowledge Management team; set vision, priorities, and ways of working.
- Own the roadmap for R&D knowledge representation (knowledge graph modeling patterns, ontology strategy, semantic layer standards).
- Establish shared language/identity/semantic consistency for enterprise assets and processes.
- Promote standard taxonomies and identifiers; ensure semantic asset quality, reuse, and stewardship.
- Partner with platform, governance, and product teams so semantic assets are discoverable, versioned, governed, and consumable via retrieval pipelines and APIs.
- Shape AI-ready data via integration/semantics/reusable data products grounded in real R&D use cases.
- Define retrieval strategies for agentic AI and user-facing GenAI apps; guide retrieval pipelines for QA/reasoning/insight.
- Establish best practices for RAG in regulated scientific environments.
- Harmonize access to enterprise data; define standards for data interfaces and retrieval APIs.
- Define data provenance/traceability/governance principles in AI-assisted scientific workflows.
Qualifications
Required
- Training/experience in life sciences, biomedical research, or pharmaceutical R&D.
- Experience leading and developing technical teams (hiring, coaching, performance expectations).
- Success leading cross-functional programs aligning product/platform/governance stakeholders.
- Advanced degree in CS, data science, computational biology, bioinformatics, or related field.
- Deep experience designing data architectures/knowledge systems for AI/ML; strong IR/semantic search/RAG expertise.
- Experience with large-scale enterprise data platforms; ability to collaborate across technical/scientific/platform teams.
Preferred
- Familiarity with omics, clinical data, literature, and regulatory documents.
- Knowledge graph/R&D knowledge system experience.
- Exposure to GenAI platforms, agentic systems, and MCPs; ontology engineering and semantic modeling; semantic layer operationalization.
Benefits (time off)
- Vacation 120 hours/year; Sick time 40 hours/year (48 in CO; 56 in WA); Holiday pay 13 days/year; Work/Personal/Family up to 40 hours/year; Parental Leave 480 hours; Bereavement Leave 240 hours (immediate family) / 40 hours (extended family); Caregiver Leave 80 hours (52-week rolling) / 10 days; Volunteer Leave 32 hours/year; Military Spouse Time-Off 80 hours/year.