Applied AI & Data Product Scientist

JPMorganChase•Jersey City, NJ

About The Position

As a Vice President and individual contributor on the Finance Transformation and Analytics team within Asset and Wealth Management Finance, the Applied AI & Data Product Scientist builds AI-driven analytics and decision-support products for Finance. The role is approximately 60% hands-on building and 40% strategy, roadmap, and best-practice development. It owns two complementary mandates: researching, prototyping, and hardening next-generation AI capabilities for production, and diagnosing and resolving structural data problems affecting AI and traditional reporting products. The scientist partners with Finance stakeholders, data engineering, and platform teams, while focusing on writing code, training models, designing data architectures, and delivering products. The role also develops a forward-looking AI and data roadmap aligned with Finance priorities and the firm’s technology direction, and measures and communicates the business impact of products and data improvements.

Requirements

At least 8 years of experience in data science, AI/ML, analytics, or product engineering, including hands-on delivery of production AI or 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 key performance indicator 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.
Bachelor’s degree in Computer Science, Engineering, Data Science, Mathematics, Statistics, Finance, or a related quantitative field.

Nice To Haves

Experience building multi-agent systems, autonomous workflows, or task planners using frameworks such as LangGraph, LangChain, or LlamaIndex.
Hands-on experience building generative AI products with guardrails, evaluation harnesses, and human-in-the-loop review grounded in approved internal sources.
Experience designing and governing semantic layers, metric stores, and data observability frameworks that support business intelligence and AI consumption.
Track record of designing and operationalizing experimentation programs, including A/B testing and causal measurement, to quantify product and data initiatives.
Knowledge of model safety, bias mitigation, privacy techniques, and AI governance frameworks in financial services.
Familiarity with modern business intelligence and visualization tools such as Tableau, Power BI, or ThoughtSpot.
Prior experience in a Finance transformation or technology function supporting front office or investment management.

Responsibilities

Lead research and rapid prototyping of emerging AI capabilities, including agentic workflows, multi-agent orchestration, retrieval-augmented generation, and advanced forecasting, and convert validated proofs of concept into production-grade products.
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 them with APIs, microservices, reporting tools, and cloud platforms.
Design evaluation harnesses, guardrails, and human-in-the-loop review processes for AI products 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 to support a single source of truth.
Define and implement a data catalogue strategy by inventorying assets, consolidating duplicative pipeline logic, standardizing metric definitions, and establishing 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.
Define and own a forward-looking AI and data roadmap aligned with 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.
Communicate roadmap, trade-offs, and results to senior leaders through clear narratives and data-driven recommendations.