AI-Driven Public Finance Quant Developer

Morgan Stanley; New York (NY); On-site; USD 150,000 - 200,000; Full time

Job summary

Morgan Stanley is seeking a highly skilled quantitative engineer to build models and production-grade applications for Public Finance. You will develop end-to-end data-driven tools, combining modeling with real-time dashboards and automated workflows.

You will work with Python, React, and AI toolchains to extract terms from documents, flag anomalies, and improve risk insight. Expect a rigorous, impact-driven environment with collaboration across traders and strategists.

Qualifications

• Master's or PhD in a quantitative field, or strong undergraduate with hands-on experience.
• Strong Python - you write clean, testable, production-ready code
• CI/CD fluency: Git, Docker, structured development workflow
• Solid statistics and probability fundamentals
• Some exposure to fixed income (or you learn fast and ask good questions)
• Experience using GenAI coding tools (Copilot, Cursor, etc.)

Responsibilities

• Build a model that helps the desk understand where risk is quietly accumulating
• Ship a React dashboard that puts that model's output in front of the right people in real time
• Automate a workflow that used to take someone two hours and now takes two seconds
• Integrate an AI agent that reads deal documents, extracts key terms, and flags anomalies before anyone else notices
• Question an assumption the business has been making for years - with data - and be right

Skills

Python

CI/CD

React

GenAI tools

Statistics

Docker

Fixed income

Education

Master's or PhD in a quantitative field

Strong undergraduate background in quantitative field

Tools

Git

Docker

kdb+/q

Job description

Morgan Stanley is seeking a highly skilled quantitative engineer to build models and production-grade applications for Public Finance. You will develop end-to-end data-driven tools, combining modeling with real-time dashboards and automated workflows.

You will work with Python, React, and AI toolchains to extract terms from documents, flag anomalies, and improve risk insight. Expect a rigorous, impact-driven environment with collaboration across traders and strategists.