Control Manager Lead, HR Data & AI Controls, Executive Director

As an Executive Director Control Manager within HR Control Management (HR/EX CDAO-aligned) you drive end-to-end controls coverage across the HR/Ex (experience) data and analytics lifecycle. You translate enterprise risk expectations into pragmatic measurable standards that are embedded by design into delivery. You will lead governance escalation and issue remediation to keep controls audit-ready and responsive to emerging AI and agentic risks. You willestablish and maintain a forward-looking control framework building strong partnerships with HR/EX CDAO and broader HR stakeholders in a highly matrixed environment strengthening governance and controls for HR data management HR data products and HR use of AI/ML capabilities as usage expands.

HR Control Management (CM) maintains a strong and consistent control environment through a joint accountability model and focuses on four areas: Risk Identification & Assessment Control Design & Evaluation Issues & Control Deficiencies and Control Governance & Reporting. The team partners closely with HR/EX Data & AI and data product stakeholders to drive consistent data governance operational discipline and evidence-based controls across sensitive HR/EX data assets.

Job Responsibilities:

• Ensure critical HR/EX data assets have clear accountability documented controls and measurable health metrics through effective data controls governance.
• Partner with HR Data & AI leaders to identify and assess risks providing control design expertise to mitigate data- and AI-related risks.
• Ensure controls are embedded by design into the data and analytics delivery lifecycle including change management access provisioning data quality controls lineage and metadata management and ongoing lifecycle performance and resiliency considerations for critical HR/EX data assets.
• Work in partnership with the Data product to design and operationalize processes from a risk management perspective ensuring appropriate controls documentation data dictionaries/definitions and evidence capture aligned to firmwide requirements.
• Partner closely with HR CDAO stakeholders to establish oversight aligned to AI and model usage growth including appropriate guardrails for development validation monitoring (including monitoring of data drift data quality degradation and upstream dependency health for high-impact HR/EX data products) and use-case change control.
• Ensure the control environment adapts to agentic AI patterns including workflow autonomy toolchain dependencies prompt and context handling and monitoring expectations appropriate to risk.
• Provide governance leadership including preparing control materials for senior forums delivering clear risk narratives and escalating material control gaps with proposed remediation paths and interim risk acceptance recommendations when appropriate.
• Partner with accountable stakeholders in the control issue lifecycle end-to-end ensuring timely remediation credible root cause analysis and sustainable corrective actions validated through effective testing.
• Ensure there is cross-functional engagement across business operations legal compliance risk audit regulators and technology control functions as well as provide support for additional control programs (e.g. OLO IAS third-party management) as needed.

Required qualifications capabilities and skills

• Bachelors degree or equivalent experience
• 7 years of financial services experience in controls audit quality assurance risk management or compliance
• Deep working knowledge of a large financial institutions risk and control framework including governance control design control testing issue management and audit/regulatory engagement
• Demonstrated senior experience leading control programs (data governance AI governance product governance or technology governance) in complex operating environments with material data risk and significant stakeholder scrutiny using strong critical thinking and analytical skills
• Proven people leadership capabilities including hiring coaching performance management and developing a high-performing team with direct accountability for outcomes across multiple dimensions
• Ability to communicate with precision and executive presence translating technical and control topics into business-relevant decisions trade-offs and accountability; ability to influence without authority and drive closure across competing priorities
• Working knowledge of CDAO requirements and governance expectations across data and analytics with the ability to engage credibly on data lifecycle controls data quality access and entitlements lineage metadata and operational discipline supporting analytics
• Demonstrated experience partnering with Data Product Engineering and platform teams on data lifecycle governance (data quality management lineage metadata and data access/entitlements) in complex highly regulated environments
• Practical understanding of data product operating models and how to implement scalable controls and governance patterns without slowing delivery (e.g. automated evidencecapture standardized data quality checks and repeatable change control)

Preferred qualifications capabilities and skills

• Expertisein at least one HR discipline ideally Workforce Data / privacy and data laws but also other HR domainsor experience supporting HR employee data or similarly sensitive data domains given heightenedconfidentialityand privacy considerations inherent to the HR/EX environment
• Experience partnering with Data & Analytics leaders to implement scalable governance and control patterns across multiple platforms and delivery teamsand experience operating governance for HR/employee data products (or similarly sensitive domains) including establishing data quality thresholds ownership models and end-to-end lineage/metadata practices that support auditability and reliable analytics/AI consumption
• Demonstrated experience shaping second-line challenge and/or first-line control ownership practices in environments with rapid AI adoption particularly where model usage is expanding and controls must evolve without slowing delivery
• Ability tooperateeffectively in high-ambiguity environments balancing speed of delivery with control rigor and building alignment across Product Engineering Data and Business stakeholders without diluting accountability