Long Short Systematic Credit Quantitative Researcher - London - Asset Manager

Job Description

Octavius Finance are a specialist hedge fund and asset management recruitment firm, working with leading investment managers across public and private markets. Octavius Finance are recruiting for a Credit Quantitative Researcher on behalf of a London-based asset manager specialising in credit investing, long/short credit strategies, and credit hedge fund investing across global credit markets, with a primary focus on European credit.

This Credit Quantitative Researcher role sits within a credit investment team covering corporate bonds, leveraged loans, and structured credit instruments such as CLOs.

Key Responsibilities:

• Develop, implement, and maintain quantitative models for credit relative value, pricing, and risk analysis across cash and structured credit markets
• Build factor-based and statistical models for credit spread dynamics, default risk, and recovery assumptions
• Analyse large, complex datasets across corporate bonds, leveraged loans, CDS, and structured credit products (including CLO tranches)
• Support portfolio construction, optimisation, and trade idea generation across long/short credit strategies
• Develop tools for risk monitoring, stress testing, scenario analysis, and performance attribution
• Enhance pricing and valuation frameworks for illiquid or complex credit instruments
• Work closely with portfolio managers and analysts to translate quantitative outputs into actionable investment insights
• Contribute to automation and improvement of research workflows and data pipelines
• Research and prototype new quantitative approaches for credit investing, including machine learning and alternative data applications

Requirements:

• Degree in a highly quantitative discipline (e.g. mathematics, physics, engineering, statistics, computer science, finance, econometrics)
• Experience in credit markets, fixed income, or structured credit strongly preferred
• Strong programming skills in Python (or equivalent), with experience in data analysis libraries (e.g. pandas, NumPy, SciPy)
• Good understanding of credit products including corporate bonds, leveraged loans, CDS, and CLO structures
• Knowledge of statistical modelling, time series analysis, and machine learning techniques beneficial
• Familiarity with risk modelling, portfolio construction, or quantitative trading strategies
• Experience working with large financial datasets and building robust research pipelines
• Strong analytical mindset with ability to work with incomplete or noisy financial data
• Excellent communication skills and ability to work collaboratively within an investment team
• Strong interest in global credit markets and alternative investment strategies

To apply, please submit a copy of your word CV to

mailto:[email protected]