Global Head of Data & AI Governance & Oversight, SVP

The Head of Data & AI Governance and Risk is accountable for ensuring that all enterprise Data, AI, and Agentic AI capabilities are well‑governed, high‑quality, trusted, and regulator‑ready, while enabling innovation to scale safely across all lines of business.

• This role defines and maintains the enterprise policies, standards, and governance operating model for Data and AI and serves as the single global point of accountability for Data and AI–related regulatory, audit, and supervisory engagement.

• The role operates proactively, anticipating regulatory direction and strengthening the firm’s posture ahead of examinations. It actively engages in all ongoing regulatory efforts related to data, risk, and AI, partnering with appropriate bank owners to ensure coordinated execution and durable remediation.

• In partnership with each line of business, this role defines the strategic target state for Data and AI governance, ensuring clarity and consistency across ownership, stewardship, authoritative sourcing, data quality, and approval expectations.

• The role is intentionally independent of platform build, model development, and use‑case delivery. Success is measured by regulatory confidence, enterprise trust, data quality, and speed enabled through strong governance and streamlined processes.

Role Mandate

Establish and operate enterprise-wide governance, risk, and regulatory oversight for Data, AI, and Agentic AI—including authoritative data sourcing—and proactively elevate the firm’s regulatory posture while enabling streamlined, standard approval of AI capabilities across the enterprise.

Role Purpose

The Head of Data & AI Governance and Risk is accountable for ensuring that all enterprise Data, AI, and Agentic AI capabilities are well‑governed, high‑quality, trusted, and regulator‑ready, while enabling innovation to scale safely across all lines of business.

This role defines and maintains the enterprise policies, standards, and governance operating model for Data and AI and serves as the single global point of accountability for Data and AI–related regulatory, audit, and supervisory engagement.

The role operates proactively, anticipating regulatory direction and strengthening the firm’s posture ahead of examinations. It actively engages in all ongoing regulatory efforts related to data, risk, and AI, partnering with appropriate bank owners to ensure coordinated execution and durable remediation.

In partnership with each line of business, this role defines the strategic target state for Data and AI governance, ensuring clarity and consistency across ownership, stewardship, authoritative sourcing, data quality, and approval expectations.

The role is intentionally independent of platform build, model development, and use‑case delivery. Success is measured by regulatory confidence, enterprise trust, data quality, and speed enabled through strong governance and streamlined processes.

Key Responsibilities

Enterprise Data, AI & Agentic AI Governance

• Define, maintain, and evolve enterprise-wide policies, standards, and control frameworks for:

• Data governance and data management

• AI, GenAI, and Agentic AI

• Responsible AI and AI risk classification

• Third‑party and vendor AI usage

• Ensure governance applies across the full lifecycle of data and AI assets, from design through retirement.

Strategic Governance Target State (LOB Partnership)

• Partner with each line of business to define and maintain the target state for Data and AI governance aligned to enterprise standards and regulatory expectations.

• Translate enterprise governance principles into domain‑specific, actionable models.

• Provide governance leadership into Data & AI roadmaps without owning delivery or architecture decisions.

Authoritative Data Sources, Ownership & Stewardship

• Establish and operate the enterprise framework for authoritative data sources by data domain and key data element.

• Partner with data owners and data stewards to:

• Designate approved and trusted data sources

• Resolve conflicts between competing sources

• Ensure lineage, data quality, and fitness for purpose

• Ensure consistent use of authoritative data sources across analytics, reporting, and AI use cases.

Enterprise Data Competency

• Institutionalize the enterprise data governance operating model, including:

• Data ownership and accountability

• Data s…