Quantitative Analyst - Rates

UBS•New York, NY

2d•$225,000 - $242,500

About The Position

The Rates Quantitative Analytics (QA) team is a Front Office group responsible for developing and maintaining models used to value and manage risk for the firm's trading positions in interest rate, credit, and hybrid derivatives. The team collaborates with trading, IT, control functions, and other QA teams, including Equities, FX, and central infrastructure. The Quantitative Analyst develops and extends analytics-library functionality to address business and regulatory requirements, works with other QA teams on cross-asset risk-management and P&L-reporting solutions, and supports trading and control functions on modelling and quantitative matters. The role calls for strong quantitative expertise, programming proficiency, and hands-on experience modelling interest rate options and other rate products.

Requirements

Strong academic background in a quantitative field such as mathematics, physics, or engineering, at Master's or PhD level.
Programming proficiency, especially in C++ or Python.
Knowledge of quantitative finance, derivative pricing, and/or machine learning techniques.
Understanding of financial products and markets, especially interest rate derivatives.
Demonstrable hands-on experience with Interest Rate Options, Lightly Structured and Exotic Rate products, and their modelling.
Experience with calibration and risk-based P&L analysis of Interest Rate Option and Term-Structure models.
Ability to communicate complex ideas fluently and articulately.
Curiosity about using AI to improve workflows, with sound judgment in validating outputs and aligning use with policies, risk standards, and ethical practices.

Responsibilities

Develop and extend functionality within analytics libraries to address business and regulatory requirements.
Work closely with other QA teams to develop cross-asset solutions for risk management and P&L reporting.
Interact frequently with trading and control functions to provide support on modelling and quantitative matters.