Morgan Stanley

Applied Mathematician — Electronic Trading & Quantitative Finance

Morgan Stanley  •  $150k - $250k/yr  •  New York (Onsite)  •  3 hours ago
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Job Description

We are a small, team of researchers and developers. We build algorithms for trading stocks, bonds, and derivatives at Morgan Stanley. Our work sits at the intersection of stochastic control, statistics, financial derivatives and numerical methods.

We are looking for a Statistician or Control Theorist who wants to work on market problems where the mathematics really matters.

The problems we study include:

  • Hedging multi-billion-dollar transactions using derivatives and dynamic risk-transfer strategies.

  • Optimizing large bond inventories to serve thousands of clients while managing risk, liquidity, and capital.

  • Using stochastic optimal control to combine instruments with different temporal dynamics into unified market making portfolio

  • Building models to price hundreds of thousands of instruments from sparse and noisy market observations.

  • Developing derivative pricing and hedging models that are robust enough for real trading environments.

This is not a role where mathematics is decorative. We expect you to formulate models, challenge assumptions, do challenging statistical analysis of data, write simulations, to turn insights into profitable trading policies and risk management tools.

The ideal candidate is comfortable moving between theory, computation, and empirical evidence. You might come from applied mathematics, statistics, operations research, probability, theoretical physics, stochastic control, or a related field. You do not need to know every corner of finance, but you should be excited by markets as a source of difficult and beautiful mathematical problems.

To understand the flavor of the work, consider papers such as:

  • “High-Frequency Covariance Estimates with Noisy and Asynchronous Financial Data” — Aït-Sahalia, Fan & Xiu

  • “Algorithmic Market Making in Dealer Markets with Hedging and Market Impact” — Barzykin, Bergault & Guéant

If one of these papers makes sense to you, we would like to hear from you.
If both make sense to you, please apply immediately.

Requirements

  • Ph.D. in Applied Mathematics, Statistics, Operations Research, Theoretical Physics

  • Strong mathematical maturity and ability to reason from first principles.

  • Advanced Data Analysis skills

  • Ability to program well enough to turn ideas into working models or prototypes.

  • Clear communication skills: you should be able to explain complex ideas to both technical and non-technical colleagues.

  • Working knowledge of probability, statistics, stochastic calculus, optimization, or stochastic control.

  • Desire to work independently

  • A good sense of humor.

Nice to Have

  • Knowledge of derivatives mathematics such as option pricing and credit default swaps.

  • Experience with noisy data, sparse estimation, dimensionality reduction and numerical optimization

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 https://www.morganstanley.com/about-us/global-offices​ into your browser.

Expected base pay rates for the role will be between $150,000 - $200,000 per year for Associate and between $225,000 - $250,000 for Vice President 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 visithttps://www.morganstanley.com/people-opportunities/eeo

Morgan Stanley

About Morgan Stanley

Morgan Stanley (NYSE: MS) is a leading global financial services firm providing a wide range of investment banking, securities, wealth management and investment management services. With offices in 42 countries, our firm's employees serve clients worldwide including corporations, governments, institutions and individuals.

We are committed to maintaining the first-class service and high standard of excellence that have always defined the firm and everything we do is guided by our five core values: Do the right thing, put clients first, lead with exceptional ideas, commit to diversity and inclusion, and give back.

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Industry
Finance & Insurance
Company Size
10,000+ employees
Headquarters
New York, NY
Year Founded
Unknown
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