Global Head of Data & AI Governance & Oversight, SVP

State Street•Boston, MA

1d•$225,000 - $337,500

About The Position

The Head of Data & AI Governance and Risk is accountable for ensuring enterprise Data, AI, and Agentic AI capabilities are well governed, high quality, trusted, and regulator ready, while enabling safe innovation across all lines of business. The role defines and maintains enterprise policies, standards, and the governance operating model for Data and AI, and serves as the single global point of accountability for related regulatory, audit, and supervisory engagement. The role proactively anticipates regulatory direction, strengthens the firm’s posture ahead of examinations, and coordinates ongoing regulatory efforts related to data, risk, and AI with appropriate bank owners. In partnership with each line of business, it defines the strategic target state for governance, including consistent ownership, stewardship, authoritative sourcing, data quality, and approval expectations. The position is independent of platform build, model development, and use-case delivery. Success is measured by regulatory confidence, enterprise trust, data quality, and speed enabled by strong governance and streamlined processes.

Responsibilities

Define, maintain, and evolve enterprise policies, standards, and control frameworks for data governance and management; AI, GenAI, and Agentic AI; Responsible AI and AI risk classification; and third-party and vendor AI usage.
Ensure governance applies throughout the lifecycle of data and AI assets, from design through retirement.
Partner with each line of business to define and maintain a Data and AI governance target state aligned with enterprise standards and regulatory expectations.
Translate enterprise governance principles into actionable, domain-specific models and provide governance leadership to Data and AI roadmaps without owning delivery or architecture decisions.
Establish and operate the enterprise framework for authoritative data sources by data domain and key data element.
Partner with data owners and stewards to designate approved sources, resolve source conflicts, and ensure lineage, data quality, and fitness for purpose.
Promote consistent use of authoritative data sources across analytics, reporting, and AI use cases.
Institutionalize the enterprise data governance operating model, including data ownership and accountability, steward roles and responsibilities, and management of key data elements and critical data assets.
Embed data accountability into business processes across all lines of business.
Own the enterprise inventory of AI initiatives across AI, GenAI, ML, and Agentic AI.
Align AI inventories, classifications, and definitions with the NIST AI Risk Management Framework and applicable regulatory expectations.
Design and operate streamlined, tiered AI approval processes with consistent intake and classification, clear routing to required partners, and predictable approval timelines.
Monitor adherence to approval processes and improve them to reduce friction and late-stage escalation.
Partner with Model Risk Management (MRM) to ensure AI and ML use cases are appropriately classified and routed to MRM where required, and align governance workflows with MRM requirements without duplicating or owning MRM accountabilities.
Define enterprise standards for data quality measurement, monitoring, and control.
Own enterprise processes for data issues and incidents, including root cause analysis, remediation tracking, and escalation.
Partner with business and platform teams to embed preventive and detective data quality controls.
Serve as the single enterprise point of contact for regulators, audit, and external inquiries related to Data and AI.
Assess emerging regulatory expectations and drive early alignment across the firm.
Coordinate and participate in active regulatory efforts relating to data, technology, and AI with appropriate bank owners.
Lead regulatory examinations, findings, and remediation programs, such as BCBS 239 and MRAs.
Convert regulatory learnings into durable governance and process improvements.
Partner with Data Architecture, Data and AI Platforms, enablement teams, and business leaders to align governance intent with architectures, embed controls and compliant pathways, and promote consistent adoption.
Maintain independence from delivery while shaping enterprise design decisions, and evolve governance practices in response to regulatory and technology change.