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Senior Product Manager (Individual Contributor) – Controls & AI Transformation, Trusts & Estates

JPMorgan Chase · New York, NY, United States

Business StrategyImported listingfull-time3 days ago

About The Role

Role Summary

We are seeking an experienced Product Manager with a strong Controls background to lead AI-enabled product transformation within the Trusts & Estates business. This is an individual contributor (IC) role—who brings awareness to and manages risk in the business. This individual will own product strategyand end-to-end delivery while partnering closely with Controls, Legal/Compliance, Operations, Technology, Data/AI teams, and senior business stakeholders. The successful candidate will modernize and transform Trusts and Estates Controls while embedding robust governance, auditability, and risk management by design.

Key Responsibilities

  • Set product vision and strategy for AI transformation in Trusts & Estates Controls.
  • Own the roadmap and prioritization, translating business goals, regulatory obligations, and risk appetite into sequenced delivery plans (12–24 months) with clear outcomes and KPIs.
  • Lead discovery and define requirements: customer/user journeys, problem statements, use-case intake, value sizing, control impact assessment, and success metrics.
  • Embed controls into product design: governance workflows, approvals, audit trails, evidence capture, access controls, data handling requirements, and change management readiness.
  • Partner with engineering and data teams to deliver scalable, secure, and observable AI capabilities (e.g., retrieval/search over approved content, workflow automation, decision-support, quality checks).
  • Drive stakeholder alignment and decision-making across business, Controls, Compliance, Legal, Risk, Operations, and Technology; manage trade-offs transparently.
  • Own delivery execution using agile product practices: epics/user stories, acceptance criteria, release planning, dependency/risk management, and post-release measurement.
  • Champion responsible AI adoption: monitoring/feedback loops, human-in-the-loop controls where needed, and clear user guidance to reduce operational and conduct risk.
  • Enable adoption and change: training, communications, support readiness, and continuous improvement based on usage and control outcomes.

Required Qualifications

  • 8+ years of product management experience (or equivalent experience owning complex platforms/workflows end-to-end).
  • Demonstrated experience in a controls-heavy banking environment (e.g., Risk & Controls, Compliance, Audit, Operational Risk, governance, QA).
  • Proven track record delivering AI/automation/analytics products in production (not just pilots), with measurable business impact.
  • Strong knowledge of regulated process design: documentation standards, audit readiness, approvals, and evidence capture.
  • Exceptional stakeholder management and executive communication; ability to influence without authority.
  • Strong analytical and strategic skills: value sizing, prioritization frameworks, KPI/OKR design, and data-driven decisioning.

Preferred Qualifications

  • Experience in wealth management, private banking, fiduciary, trust administration, or estates domains.
  • Experience with operating model design (intake-to-scale, control gates, support model, training).

Core Competencies

  • Controls mindset + innovation: balances speed with safety; designs “controls by default,” not bolted on later.
  • Strategic product leadership (IC): creates clarity from ambiguity; sets direction and drives execution through others.
  • Stakeholder leadership: builds alignment across business, Technology, and second line partners; navigates conflict constructively.
  • AI transformation leadership: understands what it takes to productionize AI responsibly (governance, monitoring, adoption, and change).

Success Measures (First 6–12 Months)

  • Delivery of prioritized AI-enabled capabilities that reduce cycle time and rework in Trusts & Estates workflows.
  • Demonstrable improvements in control effectiveness (e.g., better evidence capture, fewer exceptions, improved QA outcomes).
  • Adoption metrics (active users, frequency, task completion) and measurable productivity/quality impact.
  • Strong governance posture: audit-ready documentation, clear approvals, and stable operational support model.

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