Enterprise AI Governance Leader (Remote)

Synchrony; Stamford (CT); Hybrid; USD 200,000 - 330,000; Full time

Job summary

Synchrony seeks an SVP Head of AI Governance to lead the enterprise AI governance function, align with risk management expectations, and partner across legal, compliance, IT, and business lines. You will design and manage AI governance controls, reporting, and assurance activities to scale AI responsibly.

You will collaborate with 2nd/3rd line stakeholders, translate policies into practical procedures, and drive risk-based governance across AI lifecycle, with oversight of intake, risk tiering,

Qualifications

• Bachelor’s degree or equivalent in a related field.
• 10+ years in risk management, governance, or compliance leadership.
• Experience leading governance programs in complex enterprises.
• Strong understanding of AI, data, and regulatory expectations.
• Ability to translate policy into practical operating models and controls.

Responsibilities

• Lead enterprise AI Governance within the AI Office, including model oversight and controls.
• Collaborate with risk, compliance, legal, and security teams to strengthen AI risk management.
• Prepare for regulatory activities and executive governance updates.
• Develop and implement AI governance topics, workflows, and lifecycle gates.
• Drive continuous improvement of governance capabilities and evidence quality.

Skills

Risk governance

AI governance

Stakeholder management

Regulatory readiness

Executive communication

Program leadership

Education

Bachelor’s degree or equivalent

Tools

SharePoint

Jira

Power BI

ServiceNow

GRC platforms

Job description

Synchrony seeks an SVP Head of AI Governance to lead the enterprise AI governance function, align with risk management expectations, and partner across legal, compliance, IT, and business lines. You will design and manage AI governance controls, reporting, and assurance activities to scale AI responsibly.

You will collaborate with 2nd/3rd line stakeholders, translate policies into practical procedures, and drive risk-based governance across AI lifecycle, with oversight of intake, risk tiering,