Vice President, Data Enablement and AI Data Readiness

Boston Scientific•Georgetown, MA

1d•$225,900 - $429,200

About The Position

Boston Scientific was recognized as a Glassdoor Best Place to Work in 2026, ranking No. 15 on the Top 100 list, reflecting the culture our employees experience every day. We are seeking a Vice President, Data Enablement and AI Data Readiness to define and deliver Boston Scientific's enterprise data strategy through two integrated pillars: data governance and AI-ready data delivery. This leader will advance the organization from traditional data assets to FAIR, trusted and AI-ready data products that are findable, accessible, interoperable, reusable, well-governed and ready to support scaled analytics, machine learning, generative AI and agentic AI. This is a strategic builder-operator role with accountability for governance discipline, technology enablement and measurable delivery. You will shape a multiyear vision, align leaders across divisions, regions and functions, and drive execution through a team of senior leaders. The role will modernize data architecture, platforms, metadata, lineage, quality, access, stewardship and product management practices so governed data can be put to work quickly, safely and repeatedly. This is an opportunity to establish an enterprise capability with broad and lasting impact — creating the trusted data foundation that enables better decisions, accelerates innovation and expands Boston Scientific's ability to responsibly apply AI at scale.

Requirements

Bachelor's degree in computer science, data science, information systems, engineering, business or a related field.
Minimum of 20 years' experience in data management, data governance, analytics, data engineering or related disciplines, including a minimum of 5 years' experience leading directors, managers or other senior leaders in a large or complex organization.
Demonstrated experience owning enterprise data governance at scale, including policy, stewardship, data ownership, master data management, data quality, metadata, lineage, privacy and controls.
Demonstrated experience transforming enterprise data assets into FAIR, trusted or AI-ready data products and partnering with analytics, data science, engineering, architecture or AI teams to enable production use cases.
Strong knowledge of modern data architectures, platforms and practices, including data products, data mesh, data contracts, semantic layers, observability, lineage and metadata management.
Proven ability to set enterprise strategy and translate that strategy into execution across multiple teams, with measurable business and technology outcomes.
Executive-level communication and influencing skills, with the ability to translate complex technical concepts for business audiences and build alignment among senior stakeholders.
Working knowledge of data privacy, security, responsible data use and regulatory requirements within complex, global or regulated environments.

Nice To Haves

Master's degree or other advanced degree in a relevant technical, data or business discipline.
Experience establishing or scaling a data-as-a-product operating model within a large enterprise.
Experience with AI and generative AI data ecosystems, including areas such as retrieval-augmented generation, feature stores, AI data pipelines or model data governance.
Experience leading enterprise data transformation within a highly regulated, global organization.

Responsibilities

Define and deliver a multiyear enterprise data strategy integrating data governance with the evolution of enterprise data assets into FAIR, trusted and AI-ready data products.
Build alignment and executive sponsorship across divisions, regions and functions around a shared data strategy, roadmap and operating model.
Translate business, regulatory, technology, analytics and AI priorities into clear investment priorities and an executable roadmap that balances governance rigor, speed and modernization.
Establish enterprise measures for data health, trust, adoption, risk, reuse and AI readiness, providing executive leadership with clear visibility into progress and value.
Own and mature the enterprise data governance framework, including policies, standards, ownership, stewardship, decision rights, controls and accountability across priority domains.
Advance master data management for enterprise-critical domains, creating authoritative, reconciled and reusable core data.
Establish measurable enterprise data quality standards supported by controls, monitoring, alerting and disciplined remediation.
Embed metadata, lineage, classification, privacy, security and responsible data-use practices throughout the data lifecycle.
Define and operationalize standards for FAIR, trusted and AI-ready data products, including ownership, documentation, metadata, lineage, quality, interoperability, access, service expectations and responsible-use requirements.
Lead the technology uplift across platforms, pipelines, catalogs, semantic layers, data contracts, observability and self-service capabilities required to make governed data discoverable, understandable, accessible and reusable.
Partner with AI, analytics, engineering, architecture and business teams to prioritize high-value use cases and accelerate the delivery of production-ready data products with measurable adoption and reuse.
Build a repeatable data accelerator capability that reduces the time required to transform fragmented or under-governed data into production-grade, AI-ready data products.
Lead, coach and develop four directors and their teams, strengthening leadership capability, attracting and retaining top talent, and fostering a high-performance and accountable culture.
Serve as a trusted partner to senior leaders across engineering, product, security, privacy, legal, risk and the business, resolving cross-functional dependencies and competing priorities.
Advance data literacy and data product management practices that enable teams across the organization to use governed data responsibly and effectively.
Champion responsible data and AI practices while ensuring alignment with applicable regulatory requirements and internal policies.