Senior Director, Knowledge Management & Retrieval Strategy
Johnson & Johnson
8 days ago
Remote friendly (Titusville, NJ)
United States
IT
Overview
We are seeking a senior leader to define and scale R&D knowledge and retrieval capabilities that power GenAI experiences across R&D.
Key Responsibilities
- Lead and grow a multidisciplinary Knowledge Management organization (Knowledge Graph, Ontology, semantic layer); 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 language, identity, and semantic consistency so systems/people/analytics/AI operate from shared truth.
- Promote standard taxonomies and identifiers across the organization.
- Establish mechanisms for quality, reuse, and stewardship of semantic assets (definitions, taxonomies/ontologies, entity models, metadata).
- Partner with platform, governance, and product teams to ensure semantic assets are discoverable, versioned, governed, and consumable via retrieval pipelines and APIs.
- Define GenAI retrieval strategies (agentic AI and user-facing applications), semantic retrieval/grounding, and retrieval pipelines for QA/reasoning/scientific insight generation; establish RAG best practices in regulated environments.
- Harmonize access to enterprise data assets; define standards for data interfaces and retrieval APIs; align GenAI needs with enterprise data infrastructure strategy.
- Partner to identify/curate high-value knowledge sources across discovery, development, clinical, and regulatory domains.
- Establish principles for data provenance, traceability, and governance in AI-assisted scientific workflows.
- Ensure retrieval systems meet standards for reproducibility, traceability, auditability, and responsible AI use in regulated environments.
Qualifications
Required:
- Formal training/experience in life sciences, biomedical research, or pharmaceutical R&D.
- Proven experience leading/developing high-performing technical teams (hiring, coaching, performance expectations).
- Demonstrated success leading cross-functional programs aligning product, platform, and governance around shared roadmaps and outcomes.
- Advanced degree (CS, data science, computational biology, bioinformatics, or related).
- Deep experience designing data architectures/knowledge systems for AI/ML.
- Strong understanding of information retrieval, semantic search, and RAG.
- Experience with large-scale enterprise data platforms.
- Ability to collaborate across technical, scientific, and platform teams.
Preferred:
- Knowledge of scientific data modalities (omics, clinical, literature, regulatory documents).
- Experience building knowledge graphs/R&D knowledge systems.
- Exposure to GenAI platforms, agentic systems, and Model Context Protocols (MCPs).
- Hands-on ontology engineering/semantic modeling and knowledge graph lifecycle practices.
- Experience defining/operationalizing a semantic layer.
Benefits (as stated)
- Eligible for Company long-term incentive program.
- Vacation: 120 hours/year; Sick time: 40 hours/year (Colorado 48; Washington 56); Holiday pay: 13 days/year; Work/Personal/Family time: up to 40 hours/year; Parental leave: 480 hours; Bereavement leave; Caregiver leave; Volunteer leave; Military spouse time-off.
We are seeking a senior leader to define and scale R&D knowledge and retrieval capabilities that power GenAI experiences across R&D.
Key Responsibilities
- Lead and grow a multidisciplinary Knowledge Management organization (Knowledge Graph, Ontology, semantic layer); 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 language, identity, and semantic consistency so systems/people/analytics/AI operate from shared truth.
- Promote standard taxonomies and identifiers across the organization.
- Establish mechanisms for quality, reuse, and stewardship of semantic assets (definitions, taxonomies/ontologies, entity models, metadata).
- Partner with platform, governance, and product teams to ensure semantic assets are discoverable, versioned, governed, and consumable via retrieval pipelines and APIs.
- Define GenAI retrieval strategies (agentic AI and user-facing applications), semantic retrieval/grounding, and retrieval pipelines for QA/reasoning/scientific insight generation; establish RAG best practices in regulated environments.
- Harmonize access to enterprise data assets; define standards for data interfaces and retrieval APIs; align GenAI needs with enterprise data infrastructure strategy.
- Partner to identify/curate high-value knowledge sources across discovery, development, clinical, and regulatory domains.
- Establish principles for data provenance, traceability, and governance in AI-assisted scientific workflows.
- Ensure retrieval systems meet standards for reproducibility, traceability, auditability, and responsible AI use in regulated environments.
Qualifications
Required:
- Formal training/experience in life sciences, biomedical research, or pharmaceutical R&D.
- Proven experience leading/developing high-performing technical teams (hiring, coaching, performance expectations).
- Demonstrated success leading cross-functional programs aligning product, platform, and governance around shared roadmaps and outcomes.
- Advanced degree (CS, data science, computational biology, bioinformatics, or related).
- Deep experience designing data architectures/knowledge systems for AI/ML.
- Strong understanding of information retrieval, semantic search, and RAG.
- Experience with large-scale enterprise data platforms.
- Ability to collaborate across technical, scientific, and platform teams.
Preferred:
- Knowledge of scientific data modalities (omics, clinical, literature, regulatory documents).
- Experience building knowledge graphs/R&D knowledge systems.
- Exposure to GenAI platforms, agentic systems, and Model Context Protocols (MCPs).
- Hands-on ontology engineering/semantic modeling and knowledge graph lifecycle practices.
- Experience defining/operationalizing a semantic layer.
Benefits (as stated)
- Eligible for Company long-term incentive program.
- Vacation: 120 hours/year; Sick time: 40 hours/year (Colorado 48; Washington 56); Holiday pay: 13 days/year; Work/Personal/Family time: up to 40 hours/year; Parental leave: 480 hours; Bereavement leave; Caregiver leave; Volunteer leave; Military spouse time-off.