Director ISC Data and AI





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BD is one of the largest global medical technology companies in the world. Advancing the world of health™ is our Purpose, and it’s no small feat. It takes the imagination and passion of all of us—from design and engineering to the manufacturing and marketing of our billions of MedTech products per year—to look at the impossible and find transformative solutions that turn dreams into possibilities.





Job Description

The Director of ISC Data & AI is accountable for establishing and driving the data foundation that underpins the end-to-end Integrated Supply Chain (ISC) Digital Transformation agenda. This role owns data governance, ownership, and stewardship across the supply chain value chain (plan, source, make, deliver), and leads the design and standardization of ISC data models, reusable data products, and a contextualized data ontology that connects supply chain data to business meaning.

The role also owns the standardization, consistency, and central registration of AI agents developed across ISC — regardless of the platform used to build them — ensuring the organization's growing portfolio of AI/agentic capabilities remains governed, auditable, interoperable, and reusable. The Director will partner closely with ISC functional leaders, TDS/Data Engineering, Enterprise Architecture, Analytics, and Responsible AI teams to ensure data and agents are trusted, discoverable, and AI-ready.

Key Responsibilities

Data Governance, Ownership & Stewardship

• Define and operationalize an ISC-wide data governance framework, including policies, standards, roles (data owners, stewards, custodians), and decision rights across all supply chain domains in partnerships with TDS and GBS (including the CRUD Processes)

• Establish a federated data stewardship model, assigning clear accountability for data quality, definitions, and lifecycle management to functional/domain owners across plan, source, make, deliver, and return.

• Drive data quality management: define KPIs/metrics for completeness, accuracy, timeliness, and consistency; implement monitoring, issue resolution, and continuous improvement processes.

• Ensure compliance with enterprise data policies, security, privacy, and regulatory requirements as they apply to supply chain data.

• Chair and actively participate in data governance councils/forums, resolving cross-functional data conflicts and prioritization.

Data Standardization Across End-to-End ISC

• Lead the definition and enforcement of common data standards, taxonomies, and master/reference data (e.g., material, supplier, location, customer, SKU) across all ISC systems and processes.

• Drive harmonization of disparate data sources (ERP, MES, WMS, TMS, planning, quality, procurement systems) into consistent, standardized definitions and formats.

• Partner with IT and enterprise architecture to retire redundant data structures and reduce fragmentation across legacy systems.

Data Modeling & Data Products

• Own the design and evolution of enterprise-grade conceptual, logical, and physical data models representing the end-to-end supply chain.

• Define and lead delivery of a portfolio of reusable, business-aligned data products (e.g., supplier performance, inventory visibility, demand signal, logistics cost-to-serve) that can be consumed across analytics, AI, and operational use cases.

• Establish data product management practices: intake, prioritization, lifecycle ownership, documentation, and consumption metrics.

• Ensure data models and products are designed for scalability, reusability, and interoperability across cloud/data platform architecture.

Data Ontology & Contextualization

• Develop and maintain a contextualized ISC data ontology/semantic layer that connects raw data to business concepts, relationships, and processes, enabling consistent interpretation across systems and teams.

• Enable knowledge graph and semantic capabilities that support advanced analytics, natural-language query, and AI/agentic use cases (e.g., root-cause analysis, scenario simulation, autonomous decision-making).

• Ensure ontology design aligns with industry frameworks/standards (e.g., APQC, GS1, SCOR) where applicable, adapted to organizational context.

AI Agent Governance, Standards & Registration

• Define and own the standard for AI agent development across ISC in compliance to BD enterprise standards — establishing common design principles, lifecycle stages (design, build, test, deploy, monitor, retire), and quality gates that apply regardless of the underlying platform (e.g., internal agent frameworks, low-code/no-code platforms, or vendor-embedded copilots).

• Establish and maintain a centralized AI Agent Registry/Catalog for ISC consistent with BD enterprise standards, capturing for every agent: business purpose, owning function, data sources/products consumed, decision rights and autonomy level, model(s) used, platform/hosting environment, risk classification, and status (development, pilot, production, retired).

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