Head of Card Customer Analytics & AI Enablement-Executive Director

JPMorgan Chase & Co.; New York (NY); On-site; USD 180,000 - 250,000; Full time

Job summary

JPMorgan Chase & Co. in New York invites a seasoned analytics executive to shape AI-enabled decisioning across the Card portfolio.

You will lead a team of analytics leaders, data scientists, and analysts to translate business strategy into scalable AI solutions that improve product strategy, customer experience, and competitive positioning. The role demands deep AI/ML expertise, strong stakeholder influence, and a proven track record of building high-performing teams while advancing responsible

Qualifications

• Master's or PhD in a quantitative field with 10+ years of analytics experience.
• Experience leading multi-disciplinary analytics teams and coaching across levels.
• Strong ability to translate between business and technical audiences.

Responsibilities

• Define and drive AI and analytics strategy for Card D&A, identifying high-value opportunities for generative AI and advanced analytics.
• Partner with Data, Product, Technology, Risk, and Finance to deliver AI/ML solutions from ideation to production deployment.
• Lead analytics supporting customer experience, product design, portfolio performance, pricing, and targeting.
• Drive measurement, experimentation, and personalization strategies to improve customer experience.

Skills

AI/ML strategy

Leadership

Stakeholder influence

Python/R

Experimentation design

Communication

Education

Master's or PhD in quantitative field

Tools

Snowflake

Databricks

MLOps/observability

Job description

Shape the future of analytics by designing and implementing solutions that empower smarter, faster decisions. This is a senior leadership role within JPMorganChase's Card business that requires technical depth in AI/ML, the ability to translate between business strategy and technical execution, and a track record of building high-performing analytics teams.

As the Head of Card Customer Analytics & AI Enablement leading the Card Customer & Strategic Analytics team, you'll lead a group of analytics leaders, data scientists, and analysts responsible for delivering AI and analytics solutions that shape product strategy, customer experience, and competitive positioning across the Card portfolio. Your role will span three core areas: setting AI and analytical direction across multiple business domains, serving as the bridge between senior business stakeholders and technical teams, and building organizational capability through talent development, inclusive culture, and operational excellence.

Job Responsibilities

• Define and drive the AI and analytics strategy for Card D&A, identifying high-value opportunities for generative AI, agentic AI, and advanced analytics to create competitive advantage
• Stay current on emerging AI/ML techniques and evaluate new capabilities, tools, and vendor offerings for practical application in the Card business
• Partner with Data, Product, Technology, Risk, and Finance to deliver AI and ML solutions from ideation through production deployment, ensuring solutions are scalable, responsible, and aligned to business needs
• Lead analytics supporting customer experience, benefits, product design, portfolio performance, and pricing and targeting strategies
• Drive measurement frameworks, experimentation (including A/B testing and causal inference), and personalization strategies that improve customer experience and benefits utilization
• Build and lead competitive intelligence capabilities that monitor market trends, competitor positioning, and industry benchmarks, partnering with external vendors and synthesizing internal and external data to give senior leaders a clear view of the competitive landscape
• Deliver forward-looking analyses that inform strategic planning and product roadmap decisions
• Serve as the connective tissue between business strategy and technical execution translating business problems into analytical frameworks and translating model outputs into executive-ready recommendations
• Define analytical priorities with senior stakeholders, interpret results, and drive data-informed decisions across product, marketing, and servicing strategies
• Lead, mentor, and develop a multi-layered team of analytics leaders, data scientists, and analysts, setting clear goals and performance expectations and providing ongoing coaching across all levels
• Champion a culture of innovation, intellectual rigor, inclusion, and collaborative problem-solving across the broader Card Data & Analytics organization

Required qualifications, capabilities, and skills

• Master's or PhD in a quantitative field and 10+ years of progressive analytics experience
• Senior leadership experience managing and developing multi-disciplinary analytics teams, including managers and individual contributors with strong coaching, org design, and talent development skills
• Strong technical foundation in AI/ML, including experience evaluating and adopting emerging techniques, guiding architecture decisions, and moving solutions from prototype to production
• Working knowledge of GenAI and agentic AI patterns, including large language models, retrieval-augmented generation, and agentic frameworks, with the ability to assess where they add value vs. simpler approaches
• Exceptional ability to translate between technical and business audiences, with a track record of influencing senior leaders and cross-functional partners
• Experience scoping and prioritizing a portfolio of analytical workstreams across multiple business domains in a large enterprise environment
• Proficiency in Python and/or R, and experience with modern data platforms such as Snowflake or Databricks
• Experience with causal inference, A/B testing, and experimentation frameworks at scale

Preferred qualifications, capabilities, and skills

• Experience in Card, consumer lending, or payments analytics, including familiarity with installment products, credit risk, or customer lifecycle management
• Experience building or leading competitive intelligence functions using alternative data, market data, or external benchmarking
• Familiarity with data architecture concepts (ingestion, modeling, governance, quality) and MLOps/observability practices
• Familiarity with responsible AI principles, model governance, and regulatory considerations in financial services
• Experience enabling analytics adoption through change management, self-service tooling, or organizational enablement
• Familiarity with Agile delivery methods and modern product practices