Director, AI Discoverability and Evidence Architecture

Role: Lead enterprise strategy and capability development for AI Discoverability and Evidence Architecture within Digital Human Health, focusing on AI Search Readiness, Generative Engine Optimization (GEO), and ontology design to enhance scientific evidence discoverability and trust. Responsibilities: define long-term vision, develop roadmaps, oversee the evolution of AI initiatives into scalable enterprise services, and establish frameworks for assessing AI discoverability, evidence gaps, and narrative vulnerabilities across brands and therapeutic areas. Drive evidence, language architecture, and metadata standards, and build monitoring capabilities for AI visibility and citation quality. Lead cross-functional teams, recruit and develop talent, and forge external partnerships to scale AI discovery globally. Qualifications: Bachelor's degree; 10+ years leading digital, AI, search, or content initiatives; deep understanding of AI/ML, GenAI, and enterprise workflows; experience translating emerging technologies into scalable strategies; strong stakeholder influence skills. Preferred: experience in pharma, biotech, healthcare, or regulated environments; familiarity with evidence architecture, semantic search, knowledge graphs, and digital content standards. High-Value: focus on AI-mediated discovery in healthcare, regulated industry experience, hybrid work setup with 25% travel, and senior leadership responsibility in a global organization.