Director ISC Data and AI

Becton Dickinson Medical Devices•Sandy, UT

About The Position

The Director of ISC Data & AI establishes and drives the data foundation for the end-to-end Integrated Supply Chain (ISC) Digital Transformation agenda. The role owns data governance, ownership, and stewardship across plan, source, make, and deliver; leads the design and standardization of ISC data models, reusable data products, and a contextualized data ontology; and governs the standardization and central registration of AI agents built across ISC, regardless of platform. The Director partners with ISC functional leaders, TDS/Data Engineering, Enterprise Architecture, Analytics, and Responsible AI teams to make data and agents trusted, discoverable, interoperable, reusable, and AI-ready. The role also leads a team of data governance, data modeling/architecture, and data product professionals; advises ISC leadership on data, AI, agent strategy, roadmaps, and investment priorities; and tracks the value and ROI of related initiatives, including token usage and opportunities to reduce high token use.

Requirements

At least 10 years of experience in data management, data governance, data architecture, or analytics.
At least 5 years of experience in supply chain, manufacturing, or operations contexts.
Proven track record leading enterprise data governance programs and data modeling initiatives at scale.
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 conceptual, logical, and physical data modeling, data architecture, and modern data platforms such as cloud data warehouses or lakehouses.
Understanding of agentic AI architecture concepts, including 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 for autonomous or semi-autonomous agents.
Ability to define technology-neutral standards that can be enforced 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.
Bachelor’s degree in Supply Chain, Data/Computer Science, Information Systems, Engineering, or a related field.

Nice To Haves

Experience building or scaling data product organizations using data mesh or data product concepts.
Experience with data ontology, knowledge graphs, or semantic layer design.
Experience establishing or governing an AI agent inventory/registry, or equivalent governance for bots, RPA, or automation assets.
Exposure to multiple agent or automation platforms, such as hyperscaler agent frameworks, RPA tools, low-code agent builders, or custom LLM-orchestration frameworks.
Prior experience in a large-scale digital transformation program.
Familiarity with ontology/semantic technologies such as RDF, OWL, and knowledge graphs.
Master’s degree preferred.
Relevant data and AI certifications are a plus.

Responsibilities

Define and operationalize an ISC-wide data governance framework, including policies, standards, roles, and decision rights, in partnership with TDS and GBS, including CRUD processes.
Establish a federated data stewardship model with clear domain-owner accountability for data quality, definitions, and lifecycle management across plan, source, make, deliver, and return.
Define data quality measures and KPIs, and implement monitoring, issue resolution, and continuous improvement processes.
Ensure supply chain data practices comply with enterprise data policies, security, privacy, and regulatory requirements.
Chair and participate in data governance councils and forums, resolving cross-functional data conflicts and prioritization.
Lead common data standards, taxonomies, and master/reference data across ISC systems and processes.
Harmonize data from ERP, MES, WMS, TMS, planning, quality, and procurement systems into consistent definitions and formats.
Partner with IT and Enterprise Architecture to retire redundant data structures and reduce fragmentation across legacy systems.
Own the design and evolution of conceptual, logical, and physical data models for the end-to-end supply chain.
Define and lead delivery of reusable, business-aligned data products for analytics, AI, and operational use cases.
Establish data product management practices for intake, prioritization, lifecycle ownership, documentation, and consumption metrics.
Ensure data models and products are scalable, reusable, and interoperable across cloud and data-platform architecture.
Develop and maintain a contextualized ISC data ontology or semantic layer connecting data to business concepts, relationships, and processes.
Enable knowledge graph and semantic capabilities for advanced analytics, natural-language queries, and AI/agentic use cases.
Align ontology design with applicable industry frameworks and standards, such as APQC, GS1, and SCOR, adapted to organizational context.
Define platform-agnostic standards for AI agent development in compliance with BD enterprise standards, including design principles, lifecycle stages, and quality gates.
Establish and maintain a centralized ISC AI Agent Registry/Catalog that records each agent’s purpose, owner, data sources, decision rights, autonomy, models, platform, risk classification, and status.
Require central registration of all ISC AI agents before production deployment.
Define agent-to-data interaction patterns requiring agents to use governed data products and the ISC ontology/semantic layer rather than ungoverned direct access to source systems.
Establish agent standards for naming, logging and observability, human escalation, error handling, prompt and tool documentation, and versioning.
Define and enforce agent risk and control tiers, aligning access management, testing, oversight, and approvals with operational risk.
Partner with Enterprise AI/Responsible AI, IT Security, and Legal/Compliance to align agent standards with enterprise AI governance, model risk management, and regulatory requirements.
Drive interoperability principles for agents developed on different platforms, including discovery, registration, and appropriate task handoffs using common protocols and shared context.
Establish an Agent Review Board or equivalent certification process for new ISC agents before production release.
Monitor deployed agents for performance, drift, redundancy, and retirement needs.
Publish reusable agent design patterns and reference architectures to support compliant, efficient agent development.
Ensure the data foundation supports GenAI, predictive and 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.
Advise ISC leadership on data, AI, and agent strategy, roadmaps, investment priorities, build-versus-buy decisions, and platform rationalization.
Track and communicate the value and ROI of data governance, AI enablement, and agent standardization, including token usage, its value relative to cost, and opportunities to reduce high usage.
Build, lead, and develop a team of data governance, data modeling/architecture, and data product professionals.
Serve as the primary liaison among ISC functional leaders, IT/Data Engineering, Enterprise Architecture, Analytics/AI, and external partners on data- and agent-related matters.
Influence and align senior stakeholders across a matrixed organization, including stakeholders over whom the role has no direct authority.