What are the responsibilities and job description for the Senior Product Associate, Securities Services - Analytics, AI & Data Strategy position at JPMorgan Chase?
Join a dynamic team at the forefront of data, analytics, and AI innovation within Securities Services. Make a direct impact by delivering business-critical solutions that drive risk management, revenue maximization, and operational excellence across our global platform.
As a Senior Product Associate in Securities Services Analytics AI & Data Strategy team, you will help deliver high-impact data and analytics solutions, collaborating with cross-functional partners to support client scorecards, liquidity exposure, deposit management, risk management, and more. Your work will ensure that critical programs are efficiently funded and executed, supporting the business’s strategic priorities.
Job responsibilities
- Partners with the Product Manager to identify new product opportunities that reflect the needs of our customers and the market through user research and discovery
- Considers and plans for upstream and downstream implications of new product features on the overall product experience
- Supports the collection of user research, journey mapping, and market analysis to inform the strategic product roadmap and provide insight on potential product features that provide value to customers
- Analyzes, tracks, and evaluates product metrics including work to time, cost, and quality targets across the product development life cycle
- Writes the requirements, epics, and user stories to support product development
- Works with the broader Analytics, AI and Data Strategy team to execute Securities Services’ data, analytics and AI deliveries across all major products and stakeholder groups
- Partners closely with AI/ML research, Data Platform, Product Management, Product Development, Quantitative Research, Sales, Business Managers, Operations, and Technology teams to scope and refine business requirements
- Provides regular status updates, monitor project performance, and manage stakeholder relationships to support the full program delivery lifecycle in Technology sprints
- Conceptualizesand design effective presentations to highlight the business benefits of analytics, AI, and data initiatives
- Acts as a secondary/supporting escalation point for product-related questions to ensure suitable resolution and management of risks such as data bottlenecks
- Tracks Securities Services’ data modernization and AI initiatives, including chairing bi-weekly governance calls with key stakeholder groups
Required qualifications, capabilities, and skills
- 3 years of experience or equivalent expertise in product management or a relevant domain area
- Proficient knowledge of the product development life cycle
- Experience in product life cycle activities including discovery and requirements definition
- Developing knowledge of data analytics and data literacy
- Experience managing projects and using project management tools (e.g., JIRA, Confluence, Microsoft Teams), including agile methodology
- Experience working with technology and/or data scientists to build analytics and AI products
- Curious, patient, hardworking, detail-oriented, and motivated to solve complex analytical problems
- Ability to work both independently and in highly collaborative team environments
- Excellent written and oral communication and presentation skills, with experience communicating effectively with diverse audiences, including senior leadership
- Strong influencing skills, able to articulate complex ideas and analytical deliveries succinctly, including presenting to large audiences
- Ability to manage multiple urgent priorities with short deadlines
Preferred qualifications, capabilities, and skills
- Comprehensive IT knowledge, including Microsoft Office; ideally Alteryx and/or Tableau for data mining and modeling
- Undergraduate degree with quantitative components (e.g., Computer Science, Data Analytics, Economics, Statistics)
- Basic knowledge of AI modeling techniques relevant to banking (e.g., supervised and unsupervised Machine Learning models), gained through work experience or university coursework