Join the Chief Data & Analytics Office (CDAO) at JPMorgan Chase and be part of a team that accelerates the firm's data and analytics journey. We focus on ensuring data quality and security while leveraging insights to promote decision-making and support commercial goals. Our mission is to modernize compliance using scalable AI, creating systems that intelligently automate data usage decisions. This role offers the chance to address complex challenges across the firm's data ecosystem and develop impactful machine learning solutions.

When someone joins our team within CDAO, they become part of a mission to modernize compliance through scalable and explainable AI. We are building a system that answers the question: “Can I use this data?”, not with guesswork, but with prediction/classification, logic, proof, and intelligent automation.

Our work sits at the intersection of applied machine learning, AI reasoning systems, and data governance. We are designing the triage layer of an intelligent decision engine that combines ML-driven classification, LLM-assisted parsing, and formal logic-based verification. This is an opportunity to tackle complex, ambiguous problems that touch every part of the firm’s data ecosystem and to build ML solutions that actually make decisions.

Job Responsibilities:

Build and integrate ML models into structured backend services (APIs, pipelines, batch processors)Write production-ready Python code to support model inference, validation, and loggingAssist in building automated workflows for data ingestion, model deployment, and metadata taggingBuild dashboards, logs, or simple UI tools to visualize and debug decision outcomesCollaborate with VP engineers and cross-functional partners to understand requirements and execute implementationParticipate in code reviews, quality assurance, and ongoing system improvement

 

Required Qualifications, Capabilities, and Skills:

Bachelor’s degree in Computer Science, Software Engineering, or related field2+ years of software development experience, ideally with exposure to ML/AI systemsStrong programming skills in Python; familiarity with web frameworks (Flask, FastAPI)Understanding of model inference lifecycles, APIs, and data validationFamiliarity with Git, CI/CD pipelines, testing, and performance profilingAbility to work independently and deliver clean, maintainable, production-quality code

 

Preferred Qualifications, Capabilities, and Skills:

Master’s degree or certifications in ML engineering, MLOps, or cloud infrastructureFamiliarity with data cataloging, tagging, or schema inference workflowsExposure to enterprise governance, compliance, or secure access systemsInterest in explainable AI, decision support tooling, and intelligent policy engines