Applied AI ML Lead, AI Data Readiness

JPMorgan Chase

Confirmed live 2 days ago High trust

Quick summary

Work type: On-site
Location: New York, NY
Posted: 78 days ago
Freshness: Confirmed live 2 days ago
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $212k

$166k most similar roles pay here $256k

This listing doesn't post a salary. Most similar roles pay $176,975–$246,150.

Based on 240 similar postings.

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About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1117 open roles on FindRole.

Listed pay typically runs $186,160–$215,000 across 7 roles with salary data.

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At a glance

TL;DR · Applied AI ML Lead, AI Data Readiness

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As the Applied AI ML Lead, AI Data Readiness within Corporate & Investment Bank Data Strategy, you will shape how data becomes usable for conversational analytics and agentic access across the organization. You will define practical standards for semantic consistency, metadata management, and data quality to ensure reliable AI-readiness at scale. Your daily work involves collaborating with product owners and engineering teams to build prototypes, improve natural-language-to-database-query systems, and resolve technical gaps in data interpretability. You will utilize expertise in Databricks Genie, Snowflake Cortex Analyst, or similar tools to develop robust data foundations. By creating scorecards and maturity models, you will translate complex technical improvements into clear executive updates. This role focuses on solving the challenge of making large-scale enterprise datasets accessible for copilots and analytics tools within a complex, regulated environment.

Skills

Artificial Intelligence Machine Learning Databricks Genie Snowflake Cortex Analyst Metadata Management Data Quality Conversational Analytics Full-stack Engineering data, lineage Prototyping

What you'll do

Define and implement an enterprise AI data readiness framework for reliable agentic consumption of data products.
Establish standards for semantic and context layers to improve consistency and interpretability across analytics experiences.
Design metadata, lineage, and data quality practices to improve discoverability and reduce ambiguity in AI analysis.
Build and iterate on 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 remediation.
Identify and close semantic and contextual gaps that prevent accurate AI-driven data access.
Create scorecards, KPIs, and maturity models to track progress and drive accountability for AI readiness.
Deliver executive updates and demonstrations that connect technical improvements to measurable business impact.

What we're looking for

Formal training or certification in applied artificial intelligence and machine learning.
5+ years of experience in applied artificial intelligence and machine learning.
Experience designing or operating agentic querying approaches, including semantic and context layers.
Expertise in metadata management and data catalog ecosystems to improve discoverability and interpretability.
Hands-on experience with conversational analytics and natural-language querying systems.
Ability to prototype and build solutions across the data and application stack.
Experience working with enterprise data platforms and stakeholder-driven prioritization.
Strong executive communication skills to deliver updates and influence roadmaps in regulated environments.

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