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.

Key Responsibilities:
Knowledge Management Leadership
- 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 semantic consistency (language, identity, shared truth) across enterprise assets and processes.
- Promote standard taxonomies and identifiers.
- Establish mechanisms for quality, reuse, and stewardship of semantic assets.
- Partner to ensure semantic assets are discoverable, versioned, governed, and consumable via retrieval pipelines and APIs.
GenAI Retrieval Architecture
- Define retrieval strategies for agentic AI and user-facing GenAI.
- Design semantic retrieval/grounding and contextual data integration approaches.
- Guide retrieval pipeline development for QA, reasoning, and scientific insights.
- Establish best practices for RAG in regulated scientific environments.
Data Interface & Platform Alignment
- Harmonize access to enterprise data; define data interface and retrieval API standards.
- Bridge R&D data users, GenAI product teams, and platform owners.
Knowledge Systems & Data Strategy
- Curate high-value knowledge sources across discovery, development, clinical, and regulatory domains.
- Define data strategies for integrating structured data, documents, knowledge graphs, and literature.
- Establish principles for provenance, traceability, and governance.
Cross-Functional Collaboration & Governance
- Translate workflow data needs into data access/retrieval architectures.
- Ensure retrieval systems support reproducibility, traceability, auditability, and responsible AI in regulated environments.

Qualifications
Required:
- Life sciences/biomedical/pharma R&D training and experience.
- Proven leadership of high-performing technical teams (hiring, coaching, performance expectations).
- Success leading cross-functional programs across product, platform, and governance.
- Advanced degree in CS, data science, computational biology, bioinformatics, or related.
- Deep experience designing data architectures/knowledge systems for AI/ML.
- Strong knowledge 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 in enterprises.
- Experience operationalizing a semantic layer to improve consistency.

Benefits (time off):
- Vacation: 120 hours/year; Sick time: 40 hours/year (CO/Washington adjusted); Holiday pay (incl. floating): 13 days/year; Work/personal/family: up to 40 hours/year; Parental leave: 480 hours within one year; Bereavement: 240 hours immediate family; Caregiver leave: 80 hours (52-week rolling period); Volunteer: 32 hours/year; Military spouse time-off: 80 hours/year.