Quantitative Analyst – Asset Allocation & Portfolio Construction (Buy-Side)

Octavius Finance

London

4 months ago

Applications closed

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An exciting opportunity has arisen to join a leading quantitative team focused on developing advanced asset allocation models and portfolio construction algorithms. This team covers all major global fixed income markets, including credit, interest rate, and foreign exchange risk, working in close collaboration with portfolio managers, traders, and colleagues across risk management, structured finance, and application development.

Key Responsibilities:

Develop innovative analytical tools and strategies for asset allocation and portfolio construction.
Collaborate with portfolio and risk managers to gather requirements and deliver customized quantitative solutions.
Program as part of a quantitative development team, contributing to a library of advanced models.
Take on leadership responsibilities, including mentoring junior analysts and driving key projects.

Required Skills and Experience:

Advanced degree in a quantitative discipline (PhD preferred) such as mathematics, finance, engineering, or a related field.
Minimum of 10 years of experience in quantitative research, preferably in fixed income on the buy-side.
Strong expertise in statistical techniques, including PCA, optimization methods (linear, quadratic, mixed integer), regression models, and practical machine learning applications.
Extensive knowledge of asset allocation methodologies, including mean-variance optimization, scenario-based models, robust allocation techniques, and Black-Litterman frameworks.
Proficiency in portfolio construction techniques across macro, sector, and security levels.
Programming expertise in Python, C++, and/or Java.
Additional experience in structured finance, credit modeling, or Monte Carlo simulations is a plus.
Proven ability to lead independent research and work effectively within a collaborative team environment.
Excellent communication and presentation skills to convey complex ideas clearly.

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