Applied Mathematician — Electronic Trading & Quantitative Finance

Morgan Stanley•New York, NY

5d•Onsite

About The Position

We are a small team of researchers and developers building algorithms for trading stocks, bonds, and derivatives at Morgan Stanley. Our work is at the intersection of stochastic control, statistics, financial derivatives, and numerical methods. We are seeking a Statistician or Control Theorist to work on market problems where mathematics is crucial. The role involves formulating models, challenging assumptions, performing statistical analysis of data, writing simulations, and translating insights into profitable trading policies and risk management tools. The ideal candidate is comfortable integrating theory, computation, and empirical evidence, and is excited by markets as a source of challenging mathematical problems.

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 Haves

Knowledge of derivatives mathematics such as option pricing and credit default swaps
Experience with noisy data, sparse estimation, dimensionality reduction and numerical optimization

Responsibilities

Formulate models
Challenge assumptions
Do challenging statistical analysis of data
Write simulations
Turn insights into profitable trading policies and risk management tools
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