Vice President Analytical Solutions – Data Readiness for AI and Generative at JPMC Candidate Experience page

Plano, Texas, United States -

Full Time

Start Date

Immediate

Expiry Date

25 Jan, 26

Salary

0.0

Posted On

27 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Product Management, Data Science, Applied AI, Data Management, Data Readiness, Metadata, Data Quality, Governance, LLM, NLP, Python, APIs, Communication, Collaboration, Prototyping, Automation

Industry

Financial Services

Description

Join us as a Vice President Analytical Solution in the Data and Analytics organization, where you’ll shape the future of AI by transforming how data is prepared and enriched for advanced analytics and generative AI. This is your opportunity to drive innovation, collaborate with top talent, and make a lasting impact on our data ecosystem. We offer a dynamic environment focused on career growth, skill development, and the chance to work on cutting-edge technology. As a Vice President Analytical Solution in the Consumer and Community Banking Data and Analytics team, you will define and deliver the strategy for AI-ready data, making structured and unstructured data interpretable and reusable for AI, analytics, and Agentic systems. You will partner with engineers, data scientists, and business teams to set direction, prototype quickly, and turn innovation into scalable enterprise capability. You will help establish next-generation standards, drive execution with product thinking, and collaborate across functions to embed readiness principles into the enterprise data lifecycle. You will work hands-on with modern AI and data-engineering stacks, using your expertise to test ideas, automate workflows, and demonstrate best practices. Your work will drive measurable outcomes for data usability, enrichment quality, and readiness maturity, translating progress into tangible value for business and AI use cases. Job Responsibilities Lead the product vision for AI-ready data, defining and delivering strategy for interpretable and reusable data Establish next-generation standards by translating AI and analytics requirements into data readiness frameworks Prototype rapidly using LLM frameworks, APIs, and tools to validate new approaches for enrichment, quality, and discovery Drive execution with product thinking, simplifying modernization efforts into clear, outcome-oriented roadmaps Collaborate across functions with data owners, stewards, AI/ML teams, and platform engineering to embed readiness principles Define KPIs for data usability, enrichment quality, and readiness maturity, translating progress into business and AI value Stay hands-on with modern AI and data-engineering stacks to test ideas, automate workflows, and demonstrate best practices Required Qualifications, Capabilities, and Skills Five years of experience or equivalent expertise in product management, data science, or applied AI roles within enterprise data environments Strong understanding of data management and readiness concepts, including metadata, lineage, data quality, and governance Working knowledge of LLM and NLP ecosystems (e.g., OpenAI, Hugging Face, LangChain, or similar frameworks) Ability to prototype in Python or notebooks, call APIs, and build lightweight experiments to validate ideas Excellent communication, presentation, and influencing skills, with the ability to collaborate and build relationships across all levels, including senior executives, in various lines of business, product, and engineering teams Preferred Qualifications, Capabilities, and Skills Experience designing or managing AI-assisted data curation or enrichment workflows Exposure to vector databases, embeddings, or RAG concepts Familiarity with data management platforms (e.g., DataHub, Collibra, Alation), data modeling, and data publishing platforms Demonstrated experience working in a highly matrixed, complex organization

Responsibilities

Lead the product vision for AI-ready data and establish next-generation standards for data readiness. Collaborate across functions to embed readiness principles into the enterprise data lifecycle.