Job description

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 questions) • Experience using GenAI coding tools (Copilot, Cursor, etc.) Why This Role Because you'll actually own things. Your models will have impact. Your apps will be used. Your ideas will be heard. You'll work alongside senior strategists, bankers and traders who will push you — and you'll push back with data. We're not looking for someone to maintain spreadsheets. We're looking for someone who will make spreadsheets obsolete. AI is not the future here — it's the present. We're actively building it into our workflows, our tools, and our thinking using frontier models, RAG architectures, and LLM-powered automation. If you want to be somewhere that takes that seriously, this is it. WHAT YOU CAN EXPECT FROM MORGAN STANLEY: At Morgan Stanley, we raise, manage and allocate capital for our clients – helping them reach their goals. We do it in a way that’s differentiated – and we’ve done that for 90 years. Our values - putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back - aren’t just beliefs, they guide the decisions we make every day to do what's best for our clients, communities and more than 80,000 employees in 1,200 offices across 42 countries. At Morgan Stanley, you’ll find an opportunity to work alongside the best and the brightest, in an environment where you are supported and empowered. Our teams are relentless collaborators and creative thinkers, fueled by their diverse backgrounds and experiences. We are proud to support our employees and their families at every point along their work-life journey, offering some of the most attractive and comprehensive employee benefits and perks in the industry. There’s also ample opportunity to move about the business for those who show passion and grit in their work. To learn more about our offices across the globe, please copy and paste morganstanley.com into your browser. Expected base pay rates for the role will be between $150,000 - $200,000 per year for Associate at the commencement of employment. However, base pay if hired will be determined on an individualized basis and is only part of the total compensation package, which, depending on the position, may also include commission earnings, incentive compensation, discretionary bonuses, other short and long-term incentive packages, and other Morgan Stanley sponsored benefit programs. Morgan Stanley is an equal opportunity employer committed to building and maintaining a workforce that is diverse in experience and background. Our recruiting efforts reflect our strong commitment to a culture of inclusion, where individuals are hired, developed, and advanced based on their skills and talents. Our workforce reflects a broad cross-section of the global communities in which we operate, bringing a variety of backgrounds, talents, perspectives, and experiences. For more information, please visit : morganstanley.com .

Requirements

Must-haves

Picture a market that finances schools, hospitals, bridges, and transit systems across America.
That's the Public Finance Business and we need someone who can think in models and ship in Python.
What You'll Actually Do
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 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 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 questions)
Experience using GenAI coding tools (Copilot, Cursor, etc.)
Why This Role
We're not looking for someone to maintain spreadsheets. We're looking for someone who will make spreadsheets obsolete.
To learn more about our offices across the globe, please copy and paste https://www.morganstanley.com/about-us/global-offices into your browser.
Our workforce reflects a broad cross-section of the global communities in which we operate, bringing a variety of backgrounds, talents, perspectives, and experiences.

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pythonreactaidockerci/cdllm

Platform: eightfold
Effort: medium

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