Applied AI ML Lead, AI Data Readiness

JPMorgan Chase & Co.•New York, NY

6d•$164,350 - $260,000

About The Position

As an Applied AI ML Lead - AI Data Readiness at JPMorgan Chase within Corporate & Investment Bank Data Strategy, you will define and deliver the data foundations that enable conversational analytics and agentic access to data at scale. You will set standards for semantic and contextual consistency, improve metadata and data quality practices, and guide teams toward measurable improvements in AI-readiness. You will translate complex technical gaps into clear roadmaps and demonstrate incremental business value through frequent prototypes and executive-ready updates.

Requirements

Formal training or certification on applied artificial intelligence and machine learning concepts and 5+ years applied experience
Experience designing or operating agentic querying approaches, including semantic and context layers and query agents (for example, Databricks Genie, Snowflake Cortex Analyst, or similar)
Proven expertise in metadata management and data catalog ecosystems, including improving discoverability and interpretability
Hands-on experience with conversational analytics and natural-language querying systems, including an understanding of common failure modes
Ability to prototype and build solutions across the data and application stack to accelerate learning and delivery
Experience working with enterprise data platforms and data products, including stakeholder-driven prioritization
Strong analytical problem-solving skills, with the ability to diagnose root causes and drive durable remediation
Demonstrated success partnering across product, engineering, and business teams in complex, regulated environments
Strong executive communication and storytelling skills, including delivering clear updates and influencing roadmaps
Ability to operate effectively in a fast-paced, iterative delivery model with measurable outcomes

Nice To Haves

Experience in financial services or other large-scale enterprise environments
Experience bridging data product management and artificial intelligence solution delivery
Full-stack engineering experience to support rapid prototyping and experimentation
Experience building or governing semantic models and metrics layers for enterprise analytics at scale

Responsibilities

Define and implement an enterprise AI data readiness framework that enables reliable agentic consumption of data products
Establish standards for semantic and context layers to improve consistency, interpretability, and reuse across analytics experiences
Design and deliver metadata, lineage, and data quality practices that improve discoverability and reduce ambiguity in AI-driven analysis
Build and iterate proof-of-concept and production-ready prototypes for conversational analytics use cases
Improve performance and reliability of natural-language-to-database-query systems by analyzing failures and driving targeted remediation
Identify semantic and contextual gaps that prevent accurate AI-driven data access, and partner with owners to close them
Partner with data product leaders and engineers to prioritize and execute quality and usability improvements across critical datasets
Define scalable AI interface patterns for copilots, agents, and analytics tools to ensure consistent user outcomes
Create scorecards, key performance indicators, and maturity models that track progress and drive accountability
Deliver regular demonstrations and executive updates that connect technical improvements to business impact