As a Senior Vice President (SVP) in the Quant team, you will provide hands-on technical leadership in structured finance credit rating modelling across RMBS, CMBS, ABD and Structured Credit. You will work with the Credit Ratings, Credit Practices, Methodology Review Function, Data Engineering and Technology teams to deliver rigorous models that meet analytical needs. Responsibilities:

Take hands-on ownership of the design, implementation and enhancement of structured finance credit models.
Lead complex model and methodology development projects through to release, coordinating technical workstreams, dependencies, timelines and stakeholders across quantitative, ratings, review and Technology teams.
Develop, maintain and enhance proprietary Python and C++ modelling libraries.
Engage with rating analysts to understand analytical challenges and emerging market trends, and translate business needs into appropriate modelling approaches.
Lead the preparation of quantitative research papers and technical documentation supporting model development and methodology enhancements.
Mentor quantitative analysts through regular technical discussions and practical support, developing their modelling skills and independent judgement.
Review colleagues' models, mathematical derivations, code and technical papers for correctness and numerical robustness; challenge assumptions, identify gaps and guide solutions to complex modelling problems.
Explain complex quantitative concepts, model assumptions, results, validation and limitations clearly and patiently to less technical colleagues, using worked examples and knowledge-sharing sessions tailored to the audience.
Develop analytically sound, transparent and interpretable approaches to complex credit risk problems, favouring simple, well-justified solutions over unnecessary modelling complexity.
Ability to use AI-assisted coding tools effectively, while maintaining strong independent programming skills and taking full responsibility for understanding, reviewing and validating generated code.
Research and assess new datasets, whether publicly available or accessible under licensing agreements, to support model development and credit analysis.
Coordinate with the Technology team to ingest new data sources and develop the associated extract, transform and load (ETL) processes.

A minimum of a master's degree in Mathematics, Engineering, Physics, Economics, Finance, Statistics, or a related quantitative discipline. A PhD is preferred.
At least 10 years of relevant professional experience in quantitative credit risk, including substantial hands-on experience developing structured finance default and loss models for RMBS, CMBS, ABS and/or CLOs. Practical experience in factor models, roll-rate models and survival models.
Strong programming skills in Python or C++ and experience writing clear research articles and/or technical documentation.
Ability to perform rigorous data analysis on large datasets.
Strong mathematical judgement and the ability to quickly grasp unfamiliar problems, supported by deep knowledge of statistical modelling, probability theory, stochastic calculus and numerical methods, including numerical integration, Monte Carlo simulation, root-finding and optimisation.
Strong modelling judgement, with a demonstrated ability to translate complex problems into simple, transparent and interpretable models without compromising analytical rigour
Excellent understanding of securitisation products.
Proven experience delivering complex quantitative projects involving multiple teams and interdependent workstreams, with strong organisational skills and the ability to anticipate bottlenecks, resolve issues and maintain momentum through review, approval and release.
Demonstrated communication and teaching skills, including the ability to make complex quantitative topics accessible to non-technical audiences.
Proven experience mentoring quantitative analysts and reviewing credit models and mathematical derivations.
Experience working within or with a credit rating agency.
For candidates whose main programming language is C++, familiarity with the Python numerical computing and data science ecosystem, including NumPy, Pandas, Scikit-Learn and SciPy.
Experience developing cloud applications (AWS preferably).
Experience using LaTeX to prepare research articles or technical documentation.

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