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.

• 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.

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.

• 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.