Graduate Quantitative Researcher

Structured Credit / Securitisation
We are seeking candidates with strong coding and quantitative backgrounds, ideally with exposure to financial or scientific programming (Python, applied mathematics, statistics, or physics modelling). Experience with AI tool implementation (e.g. Prior securitisation or structured credit knowledge is advantageous but not required.

Our client is a London-based alternative investment manager focused on structured credit and risk transfer strategies. The Analyst will join a lean and entrepreneurial team, working directly with senior investment professionals on the analysis, structuring, and pricing of synthetic securitisations, as well as contributing to the design and implementation of Python- and AI-driven tools to optimise the firm’s workflow.

This is a high-exposure role for a junior candidate looking to combine technical coding skills with financial markets experience.

Support analysis, structuring, and pricing of synthetic securitisations and other structured credit products (true sale, NPL, etc.).

~ Build, test, and enhance Python-based tools and models to improve deal analysis, reporting, and investment processes.

~ Contribute to longer-term projects involving the implementation of AI tools into the firm’s workflow.

~ Collaborate with senior team members across investment, risk, and operations functions.

0–2 years of relevant experience, or recent graduate with strong academic and technical background.

~ Proven programming ability in Python; additional languages (R, C++, SQL, MATLAB) are a plus.

~ Exposure to applied mathematics, statistics, physics modelling, or financial/quantitative programming.

~ Familiarity with securitisation, structured credit, or risk transfer transactions is a plus but not required.

~ Interest in financial markets and willingness to learn structured credit investing.

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