Quantitative Solutions Manager

As a Quantitative Solutions Manager, you will lead and deliver complex valuation and advisory engagements across a wide range of financial instruments, including derivatives and cash products across all asset classes. Your work will span both contentious and non‑contentious matters, incorporating risk‑focused projects such as default risk modelling and Expected Credit Loss (ECL) calculations. You will design and enhance advanced valuation models for complex and illiquid financial instruments, including structured products where market data is limited.

In addition, you will contribute to broader quantitative and statistical advisory initiatives. In this role, you will manage multiple client projects, ensuring high‑quality and timely delivery, while supporting Directors and Partners on engagements. You will also play an active role in business development and client relationship growth. This position offers strong professional development through hands‑on experience, senior mentorship, and support toward industry qualifications such as CFA, FRM, or CQF.

• Strong professional interest in the fields of retail and corporate credit risk, scorecard methods, internal ratings‑based models, model validation, as well as UK and European regulatory standards underpinning these areas.
• Significant credit risk experience gained ideally from a major financial institution, another professional services firm, or a credit ratings agency. Valuation experience will be an advantage.
• An interest in applying tools from finance, mathematics, and data science to provide pragmatic and robust solutions to real‑world problems.
• Strong knowledge of mathematics and statistics as applied to finance and credit risk. Hands‑on experience in credit risk modelling or the valuation of financial products.
• A master’s degree in Finance, Economics, Mathematics, Statistics, Engineering or Computer Science from a reputable university.
• Desirable previous credit risk modelling experience or the building and/or validating credit risk models obtained from within a bank or a credit ratings agency.
• Some programming skills in a high‑level language (e.g., Python, R, MATLAB, Excel VBA) and/or experience with econometric software packages (e.g., STATA, SAS).