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 will establish 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 Responsibiliities: **
- 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
- Bachelor’s 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 institution’s 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 evidence capture, standardized data quality checks, and repeatable change control)
Preferred qualifications, capabilities, and skills
- Expertise in at least one HR discipline, ideally Workforce Data / privacy and data laws, but also other HR domains or experience supporting HR, employee data, or similarly sensitive data domains, given heightened confidentiality and 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 teams and 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 to operate effectively in high-ambiguity environments, balancing speed of delivery with control rigor, and building alignment across Product, Engineering, Data, and Business stakeholders without diluting accountability