Senior Director, Knowledge Management & Retrieval Strategy

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 leads the Knowledge Management team (Knowledge Graph, Ontology, semantic layer) to enable governed, reusable, AI-ready enterprise knowledge.

Key Responsibilities:
- Lead and grow a multidisciplinary Knowledge Management organization; 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 enterprise language/identity/semantic consistency so systems and people operate from shared truth.
- Promote standard taxonomies and identifiers; establish stewardship mechanisms for 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/APIs.
- Define GenAI retrieval architectures (agentic AI and user-facing applications), semantic retrieval/grounding, and retrieval pipelines for QA/reasoning/scientific insight.
- Establish RAG best practices in regulated scientific environments.
- Harmonize enterprise data access; define retrieval APIs and standards; align GenAI product needs with data infrastructure strategy.
- Define knowledge source curation (discovery to regulatory); set data strategies integrating structured data, documents, knowledge graphs, and literature; establish provenance/traceability/governance principles.
- Ensure retrieval systems meet reproducibility, traceability, auditability, and responsible AI standards.

Qualifications:
Required:
- Formal training and experience in life sciences/biomedical research/pharmaceutical R&D.
- Experience leading technical teams (hiring, coaching, performance expectations).
- Proven success leading cross-functional programs aligned to shared roadmaps and measurable 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:
- Familiarity with omics, clinical data, literature, and 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 experience.
- Experience defining/operationalizing a semantic layer (governed concepts/metrics) across data products and analytics/AI.

Benefits:
- Vacation: 120 hours/year; Sick time: 40 hours/year (CO: 48; WA: 56).
- Holiday pay incl. floating holidays: 13 days/year.
- Work, Personal and Family Time: up to 40 hours/year.
- Parental Leave: 480 hours within one year of birth/adoption/foster care.
- Bereavement Leave: 240 hours immediate family; 40 hours extended family/year.
- Caregiver Leave: 80 hours in 52-week rolling period (10 days).
- Volunteer Leave: 32 hours/year.
- Military Spouse Time-Off: 80 hours/year.