Director, AI Readiness

Pfizer • New York City, NY

2d • $162,900 - $271,500

About The Position

Pfizer Global Supply (PGS) is transforming how data powers its end-to-end supply network. The Director, AI Data Readiness & Knowledge Engineering Lead drives PGS’s Unified Data & Knowledge Strategy, connecting structured data with semi-structured and unstructured knowledge to deliver trusted, AI-ready assets for analytics, applications, AI, and agent-enabled capabilities across PGS.

Reporting to the Data Engineering Lead, this role owns the Knowledge Engineering, Ontology & Semantic Enablement team. It defines the technology roadmap and platform strategy for unstructured and semi-structured knowledge—including knowledge graphs, vector stores, and RAG infrastructure—and serves as business owner for the semantic and knowledge layer of the PGS common data model. The role ensures that regulated content, such as SOPs, GWIs, quality and manufacturing documentation, as well as broader supply chain knowledge materials, is trusted and AI-consumable. It partners with the Data Engineering Lead, Enterprise Analytics, Data Governance, and Digital to prioritize scope, drive adoption, and deliver measurable business outcomes.

This global role involves collaboration across time zones. Periodic domestic and international travel may be required, and occasional work outside standard local hours may be necessary for global strategy sessions, design reviews, critical prototypes, architecture decisions, or delivery activities. Candidates must be authorized to work in the U.S. by any employer; U.S. work visa sponsorship is not available.

Requirements

• Bachelor’s degree in computer science, data science, information systems, engineering, analytics, or a related field and at least 8 years of relevant experience in enterprise data, AI engineering, software engineering, data platforms, analytics, or architecture; or a master’s degree and at least 7 years of relevant experience; or a PhD and at least 5 years of relevant experience.
• Demonstrated experience defining enterprise data, AI, semantic, knowledge, or platform strategies and translating them into funded roadmaps, engineering capabilities, adoption, and measurable business outcomes.
• Deep technical experience building AI-enabled applications using structured data, unstructured content, vectorized information, semantic retrieval, or RAG patterns.
• Strong understanding of data products, metadata, lineage, data quality, governance, semantic modeling, ontologies, knowledge graphs, vector stores, retrieval, evaluation, and agent-consumption patterns.
• Demonstrated experience leading selected engineering capabilities, reference implementations, reusable frameworks, or shared services within a federated delivery model.
• Experience establishing standards for AI-ready data and knowledge, including trust, explainability, traceability, security, observability, and Responsible AI considerations.
• Demonstrated ability to influence senior business and technology leaders, resolve complex cross-functional decisions, and communicate strategy, trade-offs, risk, progress, and value.
• Ability to perform complex analysis and exercise sound judgment across interdependent business, data, AI, semantic, architecture, engineering, governance, and technology considerations.
• Ability to sustain focused review of detailed strategies, technical designs, evaluations, readiness measures, risks, and investment choices where requirements may be incomplete or competing.
• Permanent work authorization in the United States; candidates must be authorized to be employed in the U.S. by any employer.

Nice To Haves

• Advanced degree in a relevant discipline.
• Experience with enterprise semantic layers, business glossaries, taxonomies, metrics layers, ontologies, knowledge graphs, semantic search, and knowledge platforms.
• Experience with embedding generation, chunking, vectorization, indexing, retrieval, reranking, grounding, evaluation, citations, and AI observability.
• Experience with AI agents, MCP, reusable agent skills, tool-access patterns, multi-agent ecosystems, or application consumption of governed enterprise data and knowledge.
• Experience with Snowflake, Cortex Search, Cortex Analyst, cloud-native platforms, microservices, APIs, or enterprise AI platforms.
• Experience defining AI readiness maturity models, certification standards, scorecards, executive dashboards, and value-realization measures.
• Experience partnering across business, Enterprise Analytics, Digital, architecture, engineering, data governance, cybersecurity, privacy, and Responsible AI teams.
• Knowledge of pharmaceutical manufacturing, supply chain, quality, and commercial processes.
• Demonstrated success mentoring engineers and building communities of practice that scale common capabilities across federated teams.

Responsibilities

• Define and own the PGS AI Data Readiness and Knowledge Engineering strategy for making structured data, unstructured information, semantic context, and enterprise knowledge trusted, reusable, and consumable by users, analytics, applications, and agents.
• Set the business vision and engineering roadmap for the PGS semantic and knowledge foundation, including business glossaries, taxonomies, metrics, ontologies, semantic models, knowledge graphs, vectorized knowledge assets, and contextual retrieval.
• Lead selected enterprise engineering capabilities and reference implementations for RAG, semantic retrieval, AI search, knowledge grounding, agent access, and application consumption where common ownership is needed for consistency and reuse.
• Establish a federated engineering model for AI-ready data and knowledge, defining centrally delivered capabilities and domain-team responsibilities supported by common standards, reusable services, technical playbooks, and certification requirements.
• Create standards and reusable patterns for ingestion, contextualization, chunking, vectorization, indexing, retrieval, reranking, grounding, citations, evaluation, observability, security, and traceability across structured and unstructured AI solutions.
• Define the enterprise approach for agent-ready information access, including governed MCP patterns, reusable agent skills, semantic interfaces, and access to certified data products and knowledge assets.
• Partner with Enterprise Analytics and data-product teams to align the semantic and knowledge strategy with the PGS common data model and enable consistent consumption across analytics, Digital applications, and AI solutions.
• Co-partner with Digital leadership on architecture, technology selection, platform engineering, production deployment, scalability, security, operations, and modernization, while retaining business ownership of priorities, standards, adoption, engineering capability outcomes, and value realization.
• Own and operationalize the PGS AI Data Readiness framework and scorecard, measuring data-product readiness, semantic maturity, knowledge connectivity, retrieval quality, agent readiness, adoption, reuse, and business impact.
• Build technical capability across PGS by mentoring engineering teams, leading design reviews, accelerating high-value prototypes, and creating communities of practice around AI-ready data, semantic technologies, and knowledge engineering.

Benefits

• 401(k) plan with Pfizer Matching Contributions and an additional Pfizer Retirement Savings Contribution.
• Paid vacation, holidays, and personal days.
• Paid caregiver/parental and medical leave.
• Medical, prescription drug, dental, and vision coverage.
• Participation in Pfizer’s Global Performance Plan with a 20% bonus target of base salary.
• Eligibility to participate in Pfizer’s share-based long-term incentive program.
• Relocation assistance may be available based on business needs and/or eligibility.

What This Job Offers

• Job Type: Full-time
• Career Level: Director