VP, AI-Driven Finance Analytics & Data Products

Aumni; Jersey City (NJ); On-site; USD 180,000 - 320,000; Full time

Job summary

JPMorganChase in New Jersey seeks a Vice President — Applied Artificial Intelligence and Data Product Scientist within Finance Transformation and Analytics. You will be an individual contributor who spends roughly 60% hands-on building and 40% shaping strategy, roadmaps, and best practices.

You will own two complementary mandates: researching, prototyping, and hardening AI capabilities and diagnosing data quality issues across reporting and analytics.

Qualifications

• 8+ years of experience in data science, AI/ML, analytics, or product engineering with hands-on delivery of production AI/ML applications.
• Hands-on experience with large language models including prompt engineering, retrieval-augmented generation, function and tool calling, embeddings, vector stores, and evaluation frameworks.
• Strong Python and SQL fluency with experience building and operating production data and inference pipelines end-to-end.
• Practical expertise with cloud data platforms such as AWS, Snowflake, or Databricks, including distributed processing and data modeling.
• Demonstrated ability to take AI or generative AI solutions from concept through prototype to scaled production.
• Experience diagnosing and fixing data quality, lineage, and governance problems in complex enterprise environments.
• Understanding of Finance functions such as financial planning and analysis, management reporting, forecasting, and KPI frameworks within asset management, wealth management, or comparable financial services.
• Proficiency in applied statistics and experimentation design with the ability to measure business impact and communicate findings to non-technical audiences.

Responsibilities

• Lead research and rapid prototyping of emerging AI capabilities — including agentic workflows, multi-agent orchestration, retrieval-augmented generation, and advanced forecasting.
• Maintain a rolling 6–12 month innovation pipeline by identifying, evaluating, and experimenting with new models, frameworks, and techniques.
• Architect, build, and scale large language model agents and AI-driven solutions for Finance workflows, integrating with APIs, microservices, reporting tools, and cloud platforms
• Design evaluation harnesses, guardrails, and human-in-the-loop review processes for every AI product from prototype through production
• Translate model outputs into user-friendly insights, analytics, and decision-support surfaces for Finance leadership and front office partners
• Audit and remediate data quality and lineage gaps across reporting and analytics products so all downstream consumers work from a single source of truth
• Define and implement a data catalogue strategy by inventorying existing assets, consolidating duplicative pipeline logic, standardizing metric definitions, and establishing clear data contracts
• Architect curated and semantic data layers that are consistent, trusted, and reusable across reporting, dashboards, analytics, and AI products
• Establish data observability practices including monitoring, alerting, and lineage tracking so issues surface before they reach end users
• Define and own a forward-looking AI and data roadmap aligned to Finance priorities and the firm's broader technology direction
• Design experimentation and measurement frameworks to quantify the business impact of AI products and data quality improvements, and communicate roadmap, trade-offs, and results to senior leaders through clear narratives and data-driven recommendations

Skills

AI/ML

Python

SQL

Cloud platforms

Data pipelines

Finance knowledge

Experimentation design

Education

Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, Statistics, Finance, or related field

Tools

LangGraph

LangChain

LlamaIndex

Tableau

Power BI

ThoughtSpot

Job description

JPMorganChase in New Jersey seeks a Vice President — Applied Artificial Intelligence and Data Product Scientist within Finance Transformation and Analytics. You will be an individual contributor who spends roughly 60% hands-on building and 40% shaping strategy, roadmaps, and best practices.

You will own two complementary mandates: researching, prototyping, and hardening AI capabilities and diagnosing data quality issues across reporting and analytics.