Applied AI ML Lead, AI Data Readiness

JPMorgan Chase

Quick summary

Work type: On-site
Location: New York, NY
Salary: $164,350–$260,000 / yr
Posted: 2 days ago
Nearby: 99+ roles within 25 mi

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $215k

This role $212k

$153k most similar roles pay here $271k

This role pays more than 60% of similar roles. Most pay $183,601–$246,150 — the shaded band above. At the midpoint, this role pays about $212k versus about $215k for comparable roles.

Based on 240 similar postings.

Employer

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 436 open roles on FindRole.

Listed pay typically runs $152,000–$215,000 across 230 roles with salary data.

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At a glance

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

Role Posting Log in to save

As an Applied AI ML Lead at JPMorgan Chase within the Corporate & Investment Bank Data Strategy team, you will define and implement a data readiness framework to enable reliable agentic consumption of data products across the organization. Your daily responsibilities include establishing standards for semantic and context layers, improving metadata management practices, and delivering prototypes for conversational analytics use cases. You will work closely with data product owners and engineering teams to prioritize and execute quality improvements, ensuring consistent user outcomes through scalable AI interface patterns. The role requires expertise in agentic querying approaches, natural-language-to-database-query systems, and experience in financial services or large-scale enterprise environments. Strong analytical skills, executive communication abilities, and the capacity for rapid prototyping are essential.

Skills

Python Java SQL Databricks Snowflake Conversational Analytics Natural Language Processing Metadata Management Data Catalogs Agentic Querying Semantic Layers Contextual Consistency CI/CD AWS Azure GCP MLOps Kubernetes Terraform

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 enhance consistency and interpretability across analytics experiences.
Design metadata, lineage, and data quality practices to improve discoverability and reduce ambiguity in AI-driven analysis.
Build proof-of-concept and production-ready prototypes for conversational analytics use cases.
Analyze failures in natural-language-to-database-query systems and drive targeted remediation efforts.
Identify semantic and contextual gaps preventing accurate AI-driven data access, partnering with owners to resolve them.

What we're looking for

Formal training or certification in applied artificial intelligence and machine learning with at least 5 years of practical experience.
Experience designing or operating agentic querying systems, including semantic layers and query agents.
Expertise in metadata management and data catalog ecosystems to enhance discoverability and interpretability.
Hands-on experience with conversational analytics and natural-language querying systems, understanding common failure modes.
Strong analytical problem-solving skills for diagnosing root causes and driving remediation in complex environments.

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