Executive Director, AI & Data Governance — Controls Strategy (Client Onboarding and Documentation Operations)

This Executive Director role is for a techno-functional controls professional who can drive transformation across the controls space—specifically within COD (Wholesale KYC Operations and Digital Document Services) while extending strategy and governance across applied AI teams and CDAO function supporting CIB Operations. The organization is undergoing significant transformation with multiple AI/ML initiatives, including Generative AI and agentic AI programs, creating the need for strong governance, clear standards, and scalable controls.
As an Executive Director within Client Onboarding & Documentation Control team, you will, apply deep technical expertise to assess, monitor, and strengthen controls for AI use cases, and provide senior-level advisory and credible challenge to stakeholders across the end-to-end organization. You will also be accountable for advancing strategic testing and thematic reviews using advanced analytics, automation, and AI—shifting from sample-based testing toward broader population-level insights where feasible—to enhance the control environment, improve operational efficiency, and provide management with actionable risk intelligence.
Job Responsibilities:
Set and drive the strategy for AI controls and data governance across Client Onboarding & Documentation and broader CDAO function supporting CIB Operations (operating model, standards, governance routines, and adoption).
Build and maintain executive-level partnerships with Operations, Technology, Product, Data, Compliance, Legal, Risk, and Audit to ensure alignment with firmwide standards and consistent outcomes.
Provide executive oversight for / Perform technical AI risk assessments across AI/ML use cases—covering model design, data quality, bias/fairness, drift/failure modes, and security—and ensure issues are driven to resolution with clear accountability. Participate in workflow conversations and contribute to developing controls at start.
Strengthen data governance control expectations for AI initiatives and data products (e.g., data quality controls, lineage/traceability, ownership and stewardship, access and entitlement controls, metadata standards, retention, and monitoring).
Direct deep-dive technical reviews of AI solutions and translate complex AI/ML risks into clear, decision-grade recommendations for senior leadership and non-technical stakeholders. Leadership will involve at contributing control proposals at design, testing and post implementation stage to protect interest of 1st Line of Defense.
Champion technology-enabled testing at scale, expanding advanced analytics / data-based testing and automation to enhance risk coverage, operational efficiency, and management insight across COD.
Provide senior advisory support on change management initiatives and large-scale transformation programs, ensuring control-by-design and sustainable governance.
Drive root-cause analysis and sustainable remediation, ensuring control gaps are prioritized appropriately, actions are tracked to closure, and thematic insights are embedded into standards and future delivery.
Build and develop a high-performing global team, including talent strategy, role design, capability build (AI governance, controls, and technical depth), coaching, and succession planning.
Represent COD Controls in executive forums, providing credible challenge, risk posture updates, and proactive guidance on emerging AI risks and regulatory expectations.
Promote innovation in controls, using Python, GenAI/LLMs and automation to design scalable solutions that improve risk management and control effectiveness. Stay current on emerging AI risks, regulatory developments, and industry best practices; act as a subject matter expert and raise organizational capability through guidance, standards, and coaching.
Required Qualifications, Capabilities and Skills :
Experience: 15+ years in data science, technology risk, controls, or related technical fields, of which 3-5 years in AI and digital transformation coverage with demonstrated progression into enterprise leadership.
Education: Bachelor’s degree in computer science, Data Science, Engineering, Mathematics, Statistics, or related field (advanced degree preferred).
Deep AI/ML knowledge: Strong understanding of ML concepts, model lifecycle, evaluation, validation/monitoring considerations, and technical risk drivers (bias, overfitting, drift, data quality). Experience with GenAI, LLMs, and/or agentic AI systems
Technical capability: Proficiency in Python for analysis and automation; ability to engage credibly with engineers/data scientists on architecture, data pipelines, and model deployment.
Executive communication: Ability to translate complex technical concepts into actionable, well-structured risk insights for senior stakeholders.
Leadership & strategic execution: Proven ability to set direction, lead global teams and cross-functional stakeholders, and drive large-scale changes in fast-paced environments.
Control mindset: Strong analytical thinking, structured problem-solving, and a proactive approach to identifying and mitigating control issues.
Familiarity with AI and data governance / responsible AI / model risk management frameworks, and practices for model validation, monitoring, and QA.
Experience in designing and implementing risk and control frameworks (specifically) for AI agents and/or LLM-based systems.