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

JPMorgan Chase
New Jersey, USA
Full-time
Director · 3–5 years experience
Onsite
Discovered 2 days ago
python

Full Job Posting

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.

Qualifications

  • 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.
  • 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.

Preferred Qualifications

  • Education: Bachelor’s degree in computer science, Data Science, Engineering, Mathematics, Statistics, or related field (advanced degree preferred).

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