Director, Supply Chain Data & AI

We are the people who give possibilities purpose

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 Supply Chain 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).

• Require all ISC AI agents — irrespective of platform or team of origin — to be registered centrally prior to production deployment, ensuring no "shadow agents" operate outside governance visibility.

• Define standard agent-to-data interaction patterns, ensuring agents consume data exclusively through governed data products and the ISC ontology/semantic layer rather than direct, ungoverned access to source systems — preserving consistency, auditability, and reuse.

• Establish consistency standards across agents for: naming conventions, logging and observability, escalation/human-in-the-loop protocols, error handling, prompt/tool documentation, and versioning — so agents behave predictably and are supportable regardless of which team or platform built them.

• Define and enforce risk and control tiering for agents including access management (e.g., informational vs. transactional vs. autonomous decision-making agents), aligning oversight rigor, testing, and approval requirements to the level of business/operational risk.

• Partner with Enterprise AI/Responsible AI, IT Security, and Legal/Compliance to ensure agent standards align with enterprise AI governance policy, model risk management, and regulatory requirements (e.g., data privacy, explainability, auditability).

• Drive platform interoperability principles so agents built on different platforms can discover, register, and — where appropriate — interoperate or hand off tasks to one another using common protocols and shared context, leveraging the ISC data ontology as the common semantic backbone.

• Establish a review/certification process (an "Agent Review Board" or equivalent) that all new ISC agents pass through before production release, validating adherence to data governance, standardization, and registration requirements.

• Monitor agent performance, drift, redundancy, and retirement throughout the post-deployment lifecycle to prevent duplicate or ungoverned agents across functions.

• Publish and maintain reusable agent design patterns and reference architectures so ISC teams can build new agents faster while remaining compliant with standards, reducing platform-specific one-off builds.

AI & Digital Transformation Enablement

• Ensure the data foundation (governance, models, products, ontology) is AI-ready — supporting GenAI, predictive/prescriptive analytics, agentic AI, and automation initiatives across ISC.

• Partner with AI/ML, analytics, and digital transformation teams to identify high-value use cases and ensure data and agent-architecture readiness for their deployment.

• Serve as the accountable owner for agent standardization and registration, ensuring the growing portfolio of ISC AI agents remains consistent, discoverable, auditable, and governed — regardless of which platform or vendor tool was used to build them.

• Act as key advisor to ISC leadership on data, AI, and agent strategy, roadmap, and investment priorities, including build-vs-buy and platform rationalization decisions for agentic AI tooling.

• Track and communicate the value/ROI of data governance, AI-enablement, and agent standardization initiatives to executive stakeholders. This should include Token usage and assessing value relative to cost and identifying opportunities to reduce high token use.

Leadership & Stakeholder Management

• Build, lead, and develop a team of data governance, data modeling/architecture, and data product professionals.

• Serve as the primary liaison between ISC functional leaders, IT/Data Engineering, Enterprise Architecture, Analytics/AI, and external partners on all data- and agent-related matters.

• Influence and align senior stakeholders across a matrixed organization without direct authority over all data sources.

Qualifications

Experience

• 10+ years of experience in data management, data governance, data architecture, or analytics, with at least 5 years in supply chain, manufacturing, or operations contexts.

• Proven track record leading enterprise data governance programs and data modeling initiatives at scale.

• Experience building or scaling data product organizations (data mesh/data product concepts) is highly valued.

• Experience with data ontology, knowledge graphs, or semantic layer design preferred.

• Experience establishing or governing an AI agent inventory/registry, or equivalent governance mechanism for bots, RPA, or automation assets, strongly preferred.

• Exposure to multiple agent/automation platforms (e.g., hyperscaler agent frameworks, RPA tools, low-code agent builders, custom LLM-orchestration frameworks) with the ability to define platform-agnostic standards.

• Prior experience in a large-scale digital transformation program.

Skills & Knowledge

• Deep understanding of end-to-end supply chain processes (plan, source, make, deliver, return) and associated data domains.

• Strong knowledge of data governance frameworks, master data management, and data quality practices.

• Working knowledge of data modeling techniques (conceptual/logical/physical), data architecture, and modern data platforms (cloud data warehouses/lakehouses).

• Familiarity with ontology/semantic technologies (e.g., RDF, OWL, knowledge graphs) and their application to enterprise data.

• Understanding of agentic AI architecture concepts: tool use/function calling, orchestration, memory/context management, human-in-the-loop design, and multi-agent interoperability.

• Familiarity with AI risk classification frameworks and responsible AI/model governance practices as applied to autonomous or semi-autonomous agents.

• Ability to define technology-neutral standards that remain enforceable across a heterogeneous, multi-platform agent landscape.

• Strong stakeholder management, influencing, and executive communication skills.

• Ability to translate technical data concepts into business value for non-technical audiences.

Education

• Bachelor's degree in Supply Chain, Data/Computer Science, Information Systems, Engineering, or related field required; Master's degree preferred.

• Relevant certifications in data and AI is a plus

Why Join Us?

To find purpose in the possibilities, we need people who can see the bigger picture, who understand the human story that underpins everything we do. We welcome people with the imagination and drive to help us reinvent the future of healthcare. At BD, you’ll discover a culture in which you can learn, grow and thrive.

We believe that when people connect in person, we learn faster, collaborate more deeply, and build a stronger culture. Join us and enjoy a culture where face-to-face collaboration supports your learning, your progress, and your success.

To learn more about BD visit .

Becton, Dickinson, and Company is an Equal Opportunity Employer. We evaluate applicants without regard to race, color, religion, age, sex, creed, national origin, ancestry, citizenship status, marital or domestic or civil union status, familial status, affectional or sexual orientation, gender identity or expression, genetics, disability, military eligibility or veteran status, and other legally protected characteristics.

Required Skills

Optional Skills

.

Primary Work Location

Additional Locations

Work Shift