Vice President AI Strategy, Client Onboarding & Service

JPMorgan Chase & Co.•Chicago, IL

3d•$185,000 - $230,000

About The Position

As an AI Strategy VP in the Client Onboarding and Service team, you will drive the end-to-end delivery of AI-enabled solutions that transform client onboarding, servicing, and contact center operations. You will partner with cross-functional teams to prioritize, implement, and scale AI solutions, ensuring alignment with business goals and measurable outcomes. We value collaboration, transparency, and a culture of continuous improvement—your leadership will help us achieve our ambitious goals and set new benchmarks for success.

Requirements

Bachelor’s degree required
15 years of industry experience in program management and change management within a large-scale organization, focusing on transformation initiatives
7 years of experience in product ownership, business analysis, service design, consulting, or operations within financial services or a regulated environment
Strong understanding of AI and machine learning algorithms, frameworks, and applications
High proficiency with Microsoft PowerPoint for executive-level documents and Excel for program analytics
Experience with Microsoft SharePoint and data visualization tools such as Alteryx or Tableau
Strategic thinking and problem-solving abilities, with proven experience applying analytical tools to develop solutions
Ability to work independently, manage multiple priorities, and deliver results in a fast-paced environment
Strong organizational and project management skills with attention to detail
Excellent interpersonal skills and ability to build effective partnerships with all levels of management across diverse functions and locations
Strong leadership and team-building skills
Excellent oral and written executive-level communication and presentation skills
Proven ability to translate business problems into clear, measurable requirements and acceptance criteria
Hands-on experience designing prompts, defining evaluation criteria, and operating human-in-the-loop review in pilot or production AI use cases
Familiarity with common AI capability patterns (retrieval grounded assistants, summarization, classification, document extraction)
Command of onboarding and service metrics and levers: cycle time, exceptions, containment, FCR, AHT, ASA, abandon, QA, CSAT/NPS
Experience interpreting offline evaluation results and online experiment outcomes to drive decisions
Excellent stakeholder management across Operations, Contact Center, Product, Data, Technology, Risk, and Compliance

Nice To Haves

Master’s degree in business administration (MBA)
Prior experience in AI/ML technology delivery
Experience leading large-scale transformation initiatives in financial services or related industries
Demonstrated ability to influence senior stakeholders and drive major change initiatives
Experience in building and mentoring high-performing teams
Familiarity with governance, design standards, and performance measurement for AI programs
Professional qualifications from reputable training institutes or professional bodies
Experience building gold sets, error taxonomies, reviewer playbooks, and calibration frameworks
Working knowledge of retrieval, grounding, and prompt safety patterns; awareness of common failure modes and mitigations
Familiarity with BI tools and KPI dashboards; ability to interpret analytics or basic SQL outputs
Background in service blueprinting, Lean/continuous improvement, or change management
Understanding of model risk management and responsible AI practices in regulated environments

Responsibilities

Lead the intake, evaluation, prioritization, and scaling of AI use cases across client onboarding, servicing, and contact center functions
Manage the AI portfolio operating model, including documentation, implementation, change management, and ongoing planning
Define and track key performance indicators (KPIs) and objectives and key results (OKRs) for program performance and outcomes
Produce and deliver executive-ready reporting on portfolio performance, outcomes, and key accomplishments
Build, lead, and mentor a high-performing team of program managers, ensuring the team has the skills, tools, and capacity to meet objectives
Drive a culture of continuous improvement through best practice sharing and lessons learned
Partner with cross-functional teams to align priorities, manage dependencies, and communicate risks and issues to leadership
Advise and influence senior leaders on portfolio priorities, capability investments, and major change initiatives
Build trusted relationships with key stakeholders to drive alignment, decision-making, and adoption of AI solutions
Remove blockers, manage escalations, and ensure timely delivery of program objectives
Design structured prompt patterns and interaction flows for agent assist, case triage, classification, summarization, and document processing
Specify retrieval and grounding approaches, define expected AI behaviors, and establish escalation criteria
Write user stories that define inputs, outputs, edge cases, and acceptance criteria grounded in operational reality
Create evaluation rubrics covering quality, factuality, helpfulness, safety, tone, and compliance
Build and maintain curated test sets with clear error taxonomies and severity definitions
Define offline evaluation standards and support online experiments (A/B tests, champion challenger)
Design Human-in-the-Loop workflows, reviewer guidelines, calibration routines, and QA sampling methods
Ensure auditability through decision logs, rationale summaries, and traceable inputs/outputs
Partner with Data and Technology teams to instrument dashboards linking AI features to business KPIs and risk indicators
Define release gates, monitor for drift and regressions, and own incident playbooks for quality or safety issues
Co-create training materials, playbooks, and quick reference guides for front-line teams
Run pilots and structured feedback loops with Operations and Contact Center leaders
Communicate impact clearly and support changes to roles, capacity, metrics, and controls
Apply privacy, appropriate use, and data handling standards throughout solution design
Design for explainability, transparency, and audit readiness
Partner with Risk, Compliance, and Model Governance to meet model risk controls and documentation requirements