Payments - Core Trade Digitization, Data and AI Delivery - Associate

JPMorgan Chase JPMorgan Chase · Banking · Singapore · Commercial & Investment Bank

This role focuses on driving client adoption of digital trade platforms and solutions within JPMorgan Chase's Payments division in APAC. It involves partnering with sales, gathering client feedback, translating insights into product requirements, supporting product development, and contributing to market research and commercialization efforts. The role also involves tracking success metrics, leveraging AI-assisted insights, and maintaining awareness of regulatory and governance standards related to AI usage. While the role supports AI-enabled automation and uses AI-assisted insights, its core function is product management and client adoption rather than direct AI/ML model development.

What you'd actually do

  1. Drive APAC Corporate and FI client adoption of Core Trade digital platforms and solutions by partnering with Coverage/Sales on client engagements, demonstrations, onboarding support, and feedback loops, and by articulating value propositions aligned to different client operating models and trade workflows.
  2. Run a strong “voice of the client” loop by capturing, synthesizing, and communicating client feedback into actionable inputs for discovery and prioritization.
  3. Translate client, market, and operational insights into clear problem statements and into scoped backlog items suitable for delivery with traceability to measurable outcomes.
  4. Support new digital product development and enhancements for Core Trade by coordinating stakeholder inputs and supporting end-to-end execution across discovery through delivery
  5. Contribute to discovery and market research by synthesizing client feedback, competitor scans, usage analytics, and operational observations, and by translating findings into decision-ready inputs for the product roadmap and quarterly planning.

Skills

Required

  • Minimum 3 years of experience in Trade Finance, digital initiatives, AI, product, business analysis, or a closely related domain.
  • Bachelor’s degree in finance, architectural technology or other related disciplines
  • Working knowledge of the product development lifecycle (discovery → delivery), including requirements definition, solution design, release readiness activities, and post-release monitoring.
  • Demonstrated comfort with data and analytics, including defining KPIs, analyzing usage and performance data, and communicating insights clearly to diverse stakeholder groups.
  • Comfort and ability to leverage AI-enabled tools and approaches for product design and delivery, with sound judgment regarding appropriate use, data sensitivity, and governance expectations.
  • Strong stakeholder management and collaboration skills, with the ability to coordinate across Product, Engineering, Operations, Coverage/Sales, and functional partners.
  • Clear communication skills, with the ability to translate complex processes into practical, decision-ready materials.
  • Strong execution discipline, including prioritization, attention to detail, and follow-through in a multi-stakeholder environment.

Nice to have

  • Experience supporting digital adoption initiatives in a client-facing context for Corporate and FI clients, including demos, enablement, and structured feedback management.
  • Familiarity with trade finance workflows and documentation concepts, or demonstrated ability to learn quickly.
  • Experience contributing to agile delivery practices (for example, sprint ceremonies and backlog refinement), including writing user stories and supporting testing and release activities.
  • Exposure to process mapping and continuous improvement approaches (for example, Lean methods) and familiarity with automation concepts, including identification of candidates for digitization and AI-enabled efficiency.
  • Experience working across APAC markets and with regionally diverse Corporate and FI client requirements and operating models, while operating from a Singapore-based hub.

What the JD emphasized

  • AI-enabled automation opportunities
  • AI-assisted insights
  • AI usage
  • model risk considerations