As a Product Director at JPMorganChase within Corporate Technology, you will set product strategy and drive the transformation of how the firm governs AI and machine learning across its lifecycle — from intake and risk review to deployment, monitoring, and decommissioning. You will lead the strategy and execution of a portfolio of AI governance capabilities that operationalize the firm’s internal AI policies, standards, and control frameworks, enabling teams to build and scale AI responsibly. You will partner across AI/ML business and engineering teams, Model Risk, Legal, Compliance, Privacy, Cybersecurity and Technology Controls, and Data Governance teams and to translate firm policy and control requirements into scalable, embedded product experiences — accelerating time-to-value for AI while strengthening trust, transparency, and resiliency. Job responsibilities • Leads the transformation of legacy governance processes into modern, self-service, controls-by-design product experiences that reduce cycle time and audit burden without weakening oversight • Defines and owns the product strategy and delivery of measurable outcomes for AI governance products and platform capabilities spanning the AI/ML lifecycle. • Drives end-to-end product delivery from discovery through launch, adoption, and continuous improvement, with clear success metrics for customer satisfaction, cycle time, and governance effectiveness • Partners with engineering and design to deliver intuitive workflows for AI asset registration, governance evidence capture, and ongoing change management. • Enables product operating rhythms, including quarterly planning, dependency management, and transparent executive-level reporting • Leads cross-functional decision-making across Model Risk Management, Legal, Compliance, Privacy, Cybersecurity and Technology Controls, Data Governance, and AI/ML business and engineering teams to ensure integrated delivery • Builds and maintains a strong feedback loop with business users, CDAO leads, and control partners, leveraging data, research, and service insights to guide iteration • Manages product lifecycle governance, including documentation, deprecation planning, and change management to minimize disruption during the transformation • Influences stakeholders through clear narratives, business cases, and trade-off frameworks that align product, risk, and technology leaders to a shared direction on responsible AI at scale Required qualifications, capabilities, and skills • 10+ years applied product management experience including delivery of governance platforms or transformation initiatives at enterprise scale • Demonstrated experience owning strategy, roadmaps, and delivery for large-scale platform or governance products used by multiple teams across an enterprise • Proven ability to lead cross-functional transformation initiatives across product, engineering, risk, compliance, legal, and operations, including sunsetting legacy processes and driving enterprise adoption of new tooling • Working knowledge of AI/ML concepts and the AI/ML lifecycle how governance and controls attach to each stage • Experience translating internal policies, standards, and control frameworks into product requirements and self-service tooling • Ability to define clear success measures (customer satisfaction, review cycle time, control effectiveness, adoption) and drive continuous improvement against them • Strong communication and influence skills, including executive-ready storytelling and stakeholder alignment in complex, control-sensitive environments • Demonstrated ability to manage ambiguity, simplify complex policy and technical problems, and make sound trade-offs under constraints Preferred qualifications, capabilities, and skills • Prior experience in AI Governance, AI/ML platforms, or Data Governance product management and transformation initiatives within a large enterprise • Awareness of the external AI governance landscape (e.g., emerging industry standards and regulatory expectations) and ability to translate that awareness into internal policy and product implications • Familiarity with GenAI-specific governance considerations, including evaluations, use of third-party and open-source models, and human-in-the-loop controls. • History of strong partnership with enterprise technology and risk teams to standardize platforms, reduce duplication, while improving customer outcomes and user experience • Experience managing product managers and establishing product standards, playbooks, and operating routines across a portfolio • Experience with adjacent control frameworks — data lineage, access controls, privacy, cybersecurity — and how they integrate with AI governance