Liquidity & Account Solutions - Analytics & AI Enablement - Vice President

JPMorgan Chase & Co.; Greater London; On-site; GBP 125,000 - 200,000; Full time

Job summary

JPMorgan Chase & Co. in London seeks a Vice President to lead the delivery of scalable analytics and AI products within Payments, bridging business stakeholders, Quantitative Research and Technology to accelerate growth and improve client outcomes.

You will own end-to-end analytics initiatives—from requirements to production—drive AI strategy, and ensure governance, compliance, and measurable business impact across liquidity management and revenue growth.

Qualifications

• Significant experience in financial services with Payments or related areas.

Responsibilities

• Lead end-to-end delivery of analytics, AI/ML, and BI solutions from requirements gathering through production deployment.
• Define and execute the AI and machine learning strategy, including use case prioritization, model lifecycle management, and Generative AI initiatives.
• Partner with Quantitative Research, Technology, and Product teams to develop, scale, and industrialize innovative AI and analytics capabilities.
• Drive the implementation and adoption of business-critical analytics solutions supporting deposits, liquidity exposure, balance sheet optimization, and revenue growth.
• Establish and maintain AI/ML governance frameworks, including documentation, validation, monitoring, bias reviews, and regulatory compliance requirements.
• Partner with Technology, Quantitative Research, and data platform teams to ensure adherence to analytics governance and firmwide standards.
• Promote self-service analytics capabilities and lead AI literacy and upskilling initiatives across business stakeholders.
• Collaborate with enterprise AI Centers of Excellence to leverage shared platforms, tools, and best practices.
• Influence cross-functional stakeholders to prioritize analytics investments and drive measurable business outcomes.

Skills

Product management

AI/ML

Data science collaboration

Stakeholder management

Cross-functional leadership

Requirements translation

Communication

Education

Undergraduate degree in quantitative discipline

Tools

JIRA

Confluence

Microsoft Teams

Tableau

Power BI

Qlik

Alteryx

Job description

Drive the future of analytics and AI within Payments by delivering scalable data products, enabling AI adoption, and transforming complex business challenges into actionable insights that accelerate growth, optimize liquidity, and enhance client outcomes.

As a Vice President within the Liquidity & Account Solutions (L&AS) Business Intelligence & Analytics team, you will lead the delivery and adoption of advanced analytics, artificial intelligence, and machine learning solutions that support strategic decision-making across the Payments business. Acting as a bridge between business stakeholders, Quantitative Research, and Technology, you will drive the end-to-end development and deployment of scalable analytics products while ensuring strong governance, compliance, and business impact. This role is critical to advancing L&AS's AI and analytics capabilities across key growth areas including deposit analytics, liquidity management, balance sheet optimization, and revenue enhancement.

Job responsibilities

• Lead the end-to-end delivery lifecycle of analytics, AI/ML, and business intelligence solutions, from requirements gathering through production deployment.
• Define and execute the AI and machine learning strategy, including use case prioritization, model lifecycle management, and Generative AI initiatives.
• Partner with Quantitative Research, Technology, and Product teams to develop, scale, and industrialize innovative AI and analytics capabilities.
• Drive the implementation and adoption of business-critical analytics solutions supporting deposits, liquidity exposure, balance sheet optimization, and revenue growth.
• Establish and maintain AI/ML governance frameworks, including documentation, validation, monitoring, bias reviews, and regulatory compliance requirements.
• Partner with Technology, Quantitative Research, and data platform teams to ensure adherence to analytics governance and firmwide standards.
• Promote self-service analytics capabilities and lead AI literacy and upskilling initiatives across business stakeholders.
• Collaborate with enterprise AI Centers of Excellence to leverage shared platforms, tools, and best practices.
• Influence cross-functional stakeholders to prioritize analytics investments and drive measurable business outcomes.

Required qualifications, capabilities & skills

• Significant experience within financial services, including Payments, Treasury Services, Securities Services, FinTech, or Corporate & Investment Banking environments.
• Proven experience building and delivering data, analytics, AI, or machine learning products in partnership with technology teams and data scientists.
• Strong product management, product development, technology, or analytics background with demonstrated ownership of complex initiatives.
• Ability to translate business requirements into scalable, production-ready analytical solutions.
• Exceptional communication, presentation, and stakeholder management skills, with the ability to influence senior leadership and diverse audiences.
• Demonstrated success leading cross-functional teams and managing multiple priorities in a fast-paced environment.
• Experience utilizing project management and collaboration tools such as JIRA, Confluence, and Microsoft Teams.

Preferred qualifications, capabilities & skills

• Undergraduate degree in a quantitative discipline, including Computer Science, Data Analytics, Economics, Statistics, Engineering, or a related field.
• Working knowledge of AI and machine learning methodologies, including supervised learning, unsupervised learning, predictive modeling, Generative AI, and Large Language Models (LLMs).
• Experience with data visualization, analytics, and reporting tools such as Tableau, Power BI, Qlik, Alteryx, or similar platforms.
• Familiarity with model governance, validation frameworks, and responsible AI practices.
  Strong understanding of enterprise data architecture, analytics enablement, and data-driven operating models.