Job description

You will shape how data becomes usable for conversational analytics and AI-assisted experiences across the organization. You will define practical standards and patterns that make data easier to discover, interpret, and use safely at scale. You will collaborate with data product owners and engineering teams to deliver prototypes and production-ready capabilities. You will demonstrate progress through measurable scorecards and clear executive updates. As an Applied AI ML , Lead - AI Data Readiness at JPMorganChase 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. Job 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 Required qualifications, capabilities and skills • 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 Preferred qualifications, capabilities and skills • 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 #LI-RB3 Build AI-ready data foundations to enable scalable conversational analytics and agentic access to data.

Requirements

Must-haves

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
Required qualifications, capabilities and skills
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
Experience in financial services or other large-scale enterprise environments
Experience bridging data product management and artificial intelligence solution delivery

Nice-to-haves

Preferred qualifications, capabilities and skills

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learning

Platform: oracle-cloud
Effort: medium