Vice President, Product Delivery Manager — Private Bank

JPMorgan Chase & Co.•Plano, TX

About The Position

This Vice President role supports product delivery for the Private Bank’s “Becoming a Client” journey with end-to-end accountability for delivering measurable outcomes across the onboarding lifecycle. The VP will drive roadmap execution, release predictability, and controls-by-design delivery across the journey, holding delivery teams accountable for timing, quality, and realized client and advisor experience improvements. The role aligns business, operations, technology, and controls stakeholders to improve client experience while meeting firm standards for KYC/AML, data privacy, and operational risk. Success requires strong product delivery discipline, clear outcome measurement, and the ability to translate strategic intent into executable delivery plans across multiple delivery teams. The VP leads a team (direct and/or matrixed) and enables partner teams to execute at pace by removing impediments, enforcing delivery rigor, and maintaining a clear line of sight from priorities to shipped product increments and realized outcomes. The VP will establish modern product delivery practices and reusable artifacts (e.g., standardized ceremonies, integrated delivery plans, risk and control traceability, definition of done discipline, and automated reporting) and will coach teams to adopt AI-assisted workflows to accelerate product discovery and delivery artifacts (including requirements, user stories, prototyping, test assets, control evidence packaging, reporting, and operationalization), while maintaining quality, auditability and control integrity.

Requirements

6+ years (or equivalent) leading large-scale, cross-functional product delivery or complex digital program management in a large financial institution or similarly regulated environment, with direct accountability for delivery outcomes, execution discipline, and predictable releases.
Proven track record of meeting timing commitments, managing critical paths and cross-team dependencies, and driving corrective actions in complex, multi-team delivery environments.
Demonstrated experience building and leading high-performing teams (direct and/or matrixed) and influencing across lines of business and control functions, including senior leadership, to drive decisions and outcomes.
Executive-ready communication, including the ability to synthesize complex topics into clear recommendations, drive decisions, and manage escalations with transparency and urgency.
Hands-on product delivery management expertise, including integrated delivery planning tied to roadmaps and releases, dependency management, release governance, and outcomes tracking. Comfort operating across both business and technology delivery teams is essential.
Experience implementing modern delivery practices (Agile and or Lean at scale) and driving operating model changes that improve throughput and predictability.
Familiarity with AI-enabled delivery practices and responsible AI concepts (not necessarily deep machine learning expertise), including an ability to operationalize guardrails, auditability, and safe adoption aligned to firm standards.

Responsibilities

Own product delivery outcomes and the integrated delivery plan for the “Becoming a Client” journey, tying work to the product roadmap and planned releases with critical path dependency, capacity, decision, and transparency artifacts (e.g., documented decisions and traceability from commitments to shipped increments and outcomes).
Drive trade-off decisions across scope, timing, capacity, and risk to protect committed releases and measurable outcomes.
Establish and run a durable operating rhythm for quarterly planning and sprint and release execution, including health reviews, release predictability, structured burn-down of impediments, proactive escalation, and corrective re-planning when delivery is at risk.
Set and run product delivery governance forums with clear ownership, decision rights, and escalation paths, providing exec-ready communications on progress, risks, dependencies, and release readiness.
Orchestrate cross-product and cross-team dependencies across onboarding steps (pre-acceptance, due diligence, documentation, account opening, product enablement, and early-life servicing handoffs), ensuring sequencing, integration, and timely decisions across the end-to-end journey.
Enforce delivery discipline and go-live readiness across teams, including backlog readiness, clear requirements and acceptance criteria, definition of done, sprint and release health, and launch validation.
Enable cross-functional delivery execution by removing blockers and aligning Product Management (strategy and prioritization) with Technology and Operations partners (build and run).
Embed controls-by-design by partnering with Compliance and Operational Risk to incorporate KYC/AML obligations, data privacy expectations, record retention needs, and auditability constraints as first-order requirements.
Operationalize controls through requirements and non-functional requirements, testing and validation, evidence collection, post-release monitoring and change management, and clear proof of control design and operating effectiveness (including procedures and training).
Identify, prioritize, and scale AI-enabled delivery and onboarding use cases that improve speed, quality, and operational effectiveness (e.g., requirements and story synthesis, workflow insights, document classification and quality checks, test asset support, control evidence packaging, and KPI automation).
Establish responsible AI adoption guardrails with Compliance, AML, Operational Risk, Information Security, Privacy, Legal, Data Governance, and Model Risk Management (as applicable), including human-in-the-loop controls, auditability and traceability of outputs, appropriate data handling, and alignment to firm standards for responsible use.
Define, track, and drive executive-ready success metrics across outcomes and delivery performance, including delivery predictability and flow, quality and rework, onboarding cycle time and data quality, controls and audit readiness, and adoption of new ways of working (including AI-assisted workflows).