J.P. Morgan Wealth Management - Executive Director, AI-Powered Wealth Growth & Sales Optimization at JP Morgan Chase – Columbus | Jobyssey

Build the next generation of sales effectiveness in CCB Wealth Management by turning funnel optimization, analytics, behavioral insights, and GenAI into advisor action at scale. Bring product leadership, operating rigor, and cross‑functional delivery skills to embed capabilities into day‑to‑day workflows with measurable impact and senior stakeholder visibility.

As an Executive Director in AI‑Powered Wealth Growth & Sales Optimization (Columbus, OH), you will own the strategy and execution agenda across the lead‑to‑conversion journey. You will operate as the quarterback across Business, Data & Analytics, and Technology, with regular engagement with CFO/CMO/CEO stakeholders.

You will prioritize the highest‑impact opportunities, define success measures, and establish a test‑and‑learn cadence that improves conversion and advisor productivity. You will champion a centralized lead operating system where leads are captured, prioritized and routed, worked through an omni‑queue, and continuously optimized through measurement and experimentation. A core mandate is to pilot and scale GenAI solutions embedded in frontline advisor workflows with controls, auditability, training, and responsible use standards.

Job responsibilities:

Set the sales optimization roadmap across the lead‑to‑conversion journey, prioritizing initiatives based on impact, feasibility, and speed to value
Own the centralized lead operating system vision, including an omni‑queue approach for prioritization and speed‑to‑lead (rule design, monitoring, tuning)
Establish success metrics and a measurement framework (conversion, cycle time, capacity, coverage, advisor productivity) with clear scorecards, definitions, and accountability
Diagnose performance using funnel analysis and large datasets to identify bottlenecks, routing‑fit issues, and working‑mechanics gaps; quantify opportunity size and define problems worth solving
Identify, pilot, and scale GenAI use cases that improve conversion and productivity (summarization, context synthesis, next‑best‑action guidance, follow‑up drafting, workflow automation)
Translate business priorities into clear requirements and acceptance criteria for analytics and technology partners (data definitions, workflow intent, delivery governance; data product patterns such as curated datasets, semantic layers, APIs)
Lead cross‑functional delivery and change management to embed new processes, tools, and GenAI capabilities into advisor workflows; monitor performance post‑launch and iterate through a closed‑loop optimization cadence

Required qualifications, capabilities, and skills

10+ years of experience in sales strategy, sales operations, growth strategy, revenue operations, business analytics, consulting, or product strategy
Senior leadership experience driving measurable growth and operational performance in a complex, regulated environment
Demonstrated ability to lead a portfolio of cross‑functional initiatives from strategy through delivery, adoption, and measurement across business, data/analytics, and engineering teams
Strong understanding of funnel performance management (lead capture, routing, prioritization, conversion, capacity planning)
Ability to analyze large datasets using tools/approaches such as spreadsheets, SQL, and BI
Experience defining success measures and measurement plans that connect work to business outcomes; strong executive communication skills
Working knowledge of GenAI/LLMs applied in sales or advisor workflows, with emphasis on governance, controls, auditability, and responsible use

Preferred qualifications, capabilities, and skills

Experience in wealth management, banking, or advisor‑led sales models, including lead distribution and referral operating mechanisms
Experience building or scaling centralized queue‑based operating models (omni‑queue) and routing logic across channels
Hands‑on experience launching GenAI‑enabled workflow tools (summarization, drafting, next‑best‑action, knowledge retrieval, workflow automation) with measurable lift
Familiarity with data integration challenges across enterprise systems and modern data product patterns (curated datasets, semantic layers, APIs)

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