Intern - Quantitative Services

LSEG•Boston, MA

1d•$44,500 - $74,100•Hybrid

About The Position

Our Quantitative Services Summer Internship Programme has been crafted so that you can understand what a career on the Quantitative Services team of Post Trade Solutions at LSEG would be like. As an intern, you play a part in the success of our organisation. This position is within the Quantitative Services team of the Post Trade Solutions division at LSEG based in Boston. The Quantitative Services team is responsible for the development of ORE, its dependent services, and implementation of ORE in major financial institutions to deliver transparent, state-of-the-art risk management.

Requirements

Must be in your penultimate year of graduate study and due to complete your degree on or before summer 2028.
Should have a predicted PhD or master’s degree in Math, Physics, Financial Engineering or other quantitative fields.
Interest in Quantitative Finance, demonstrated through studies, projects or independent work.
A good understanding of at least Python or C++.
Having functional knowledge on stochastic models/SDEs, e.g. Ornstein-Uhlenbeck/Hull-White model, Black-Scholes model, etc.
Someone who can collaborate with others, using their communication skills in the process.
A curiosity for data and an ability to analyse information to draw conclusions and propose solutions.
Knowledge of financial derivatives and pricing techniques, e.g. finite difference, Monte Carlo Simulation, etc.
Ideal candidates will have the rights to work in USA without restrictions for the duration of the internship programme.

Nice To Haves

Familiarity with Git, LaTex, is ideal but not essential.

Responsibilities

Contributing to the development of Open-Source Risk Engine (ORE).
Supporting the infrastructure for the IM Risk Generation (IMRG) service.
Pricing client portfolios and performing stress-testing and backtesting using ORE.
Utilizing ORE to calculate risk (VaR, SIMM, etc.) on client portfolios.
Translating client portfolios into the ORE XML format.
Collaborating with collateral management and risk teams to build financial applications.