Location
London, Greater London, England

Hours
Full Time

Salary
Negotiable

About the Role
We are a team of FICC Quantitative Researchers focused on transforming the Fixed Income, Currencies, and Commodities (FICC) business through quantitative trading. Our remit includes trading against the central risk book (CRB) across product types such as Interest Rates (IR), Foreign Exchange (FX), and Commodities, with trade horizons spanning hours to months. Our alpha generation is data-driven and leverages advanced mathematical techniques including machine learning and generative AI models.

In this role, you will take a leading position on our Quantitative Trading desk, bridging the gap between research and the live trading book. Responsibilities include improving monetization of systematic trading signals, translating raw predictive alpha into PnL through optimal position sizing, risk decomposition, and dynamic covariance estimation. You will implement frameworks to centrally manage risk and build optimal portfolios across FICC asset classes to enhance risk-adjusted returns.

Utilizing advanced statistical analysis and quantitative techniques such as neural networks, machine learning, and factor models, you will develop models that drive systematic alpha strategies enabling real-time trading and risk management decisions. Collaboration on the strategic development and ongoing expansion of our research infrastructure, quantitative modeling frameworks, and signal analytics engines is also key to this role.

Experience
2–10 years of quantitative research experience on the buy-side, including hedge funds, proprietary trading firms, and systematic asset managers. Proven track record of monetizing alpha signals through portfolio construction, advanced optimization, execution, and risk modeling. Thorough knowledge of quantitative investment processes and the factors driving systematic strategy returns.

About you
Self-starter with strong self-management skills and the ability to manage multiple priorities in a fast-paced environment. Excellent written and verbal communication skills, capable of articulating complex quantitative concepts to both technical and non-technical audiences.

Qualifications
Excellent academic record in a relevant quantitative discipline such as physics, mathematics, statistics, engineering, or computer science. Strong programming skills in Python (for research and signal generation) and/or Java (execution systems).