Long Short Systematic Credit Quantitative Researcher London Asset Manager

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:

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

Requirements:

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

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