Fixed Income Division – Associate, Public Finance Quantitative Developer & Strategist (New York)

Morgan Stanley is a leading global financial services firm providing a wide range of investment banking, securities, investment management and wealth management services. The Firm's employees serve clients worldwide including corporations, governments and individuals from more than 1,200 offices in 43 countries.

As a market leader, the talent and passion of our people is critical to our success. Together, we share a common set of values rooted in integrity, excellence and strong team ethic. Morgan Stanley can provide a superior foundation for building a professional career - a place for people to learn, to achieve and grow. A philosophy that balances personal lifestyles, perspectives and needs is an important part of our culture.

The Fixed Income Division is comprised of Interest Rate and Currency Products, Credit Products and Distribution. Professionals in the Division assess and actively manage risk, trade securities, and structure as well as execute innovative transactions in the fast-paced and constantly changing global markets. The Commodities Division is a market leader in energy, metals, and agricultural product trading worldwide whose professional’s trade in both physical and derivative commodity risk.

The Setup

Picture a market that finances schools, hospitals, bridges, and transit systems across America.

Now picture it moving fast — issuance calendars shifting daily, yields repricing in real time, traders making split-second calls on inventory, and originators trying to win mandates in a market where the difference between winning and losing is a few basis points and the quality of your analysis.

That's the Public Finance Business and we need someone who can think in models and ship in Python.

What You'll Actually Do

You'll be part quant, part engineer, part product thinker — and you won't be handing work off to someone else to 'make it real.' You'll take your own ideas from a concept all the way to a production application in use every day.

On any given week you might:

• 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

We use Python, Flask, React, kdb+/q, Docker, OpenAI, and Anthropic Claude — and we're building AI into everything. If that stack excites you, keep reading.

The Kind of Person We're Looking For

• You think in systems. You don't just solve the problem in front of you — you build something that solves it 1,000 times automatically, with logging, monitoring, and in a way that it can interact with every other system we build
• You ship things. Ideas are cheap. You know how to take an idea through design, development, testing, and deployment — and you care about what happens after it goes live
• You're quantitatively grounded. You understand probability, statistics, and why models fail. You're skeptical of your own outputs and even more skeptical of other people's
• You're excited about AI — not intimidated by it. You've used LLMs as a coding tool, you've thought about how to embed them into workflows, and you want to be at the frontier of what's possible in a regulated financial environment
• You communicate clearly. You can explain a complex idea to a trader who doesn't care about your methodology — only whether it works

What You'll Need

• Master's or PhD in a quantitative field, or a strong undergraduate background with demonstrated 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 qu