Senior Vice President, Quant, Structured Finance Analytics | Quantitative Analyst, Securitisation

DBRS; City of Westminster; On-site; GBP 180,000 - 260,000; Full time

Job summary

DBRS is seeking a Senior Vice President to lead the Quant team in the design and enhancement of structured finance credit models. You will own modelling libraries in Python and C++, mentor analysts, and coordinate with Technology, Ratings, and Data Analytics to deliver robust credit risk solutions.

The role requires a master’s degree in a quantitative field and 10+ years in quantitative credit risk, with deep knowledge of RMBS/CMBS/ABS/CLOs, factor models, and survival analysis.

Qualifications

• Master's degree in Mathematics, Engineering, Physics, Economics, Finance, Statistics, or related quantitative discipline.
• A PhD is preferred.

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 across quantitative, ratings, review and Technology teams.
• Develop, maintain and enhance proprietary Python and C++ modelling libraries.
• Engage with rating analysts to translate business needs into modelling approaches.
• Lead the preparation of quantitative research papers and technical documentation.
• Mentor quantitative analysts through regular technical discussions and practical support.
• Review colleagues' models, mathematical derivations, code and papers for correctness and robustness.
• Explain complex quantitative concepts to non-technical colleagues using worked examples.
• Develop transparent and interpretable approaches to complex credit risk problems; favour simple, well-justified solutions.
• Use AI-assisted coding tools effectively while maintaining strong programming skills and validating generated code.
• Research and assess new datasets to support model development and credit analysis.
• Coordinate with Technology to ingest new data sources and develop ETL processes.
• Strong mathematical judgement with knowledge of statistics, stochastic calculus and numerical methods.
• Excellent understanding of securitisation products.
• Proven experience delivering complex quantitative projects with multiple teams.
• Demonstrated communication and teaching skills.

Skills

Python

C++

Quantitative modelling

Structured finance modelling

Statistical modelling

Data analysis

Monte Carlo simulation

Mathematical judgement

Communication

Mentoring

Problem solving

Education

Master's degree in a quantitative discipline

PhD preferred

Tools

NumPy

Pandas

SciPy

LaTeX

AWS

Job description

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.

Nice to have:

• 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.

If you receive and accept an offer from us, we require that personal and any related investments be disclosed confidentially to our Compliance team. These investments will be reviewed to ensure they meet Code of Ethics requirements. If any conflicts of interest are identified, then you will be required to liquidate those holdings immediately. In addition, dependent on your department and location of work certain employee accounts must be held with an approved broker (for example all, U.S. employee accounts). If this applies and your account(s) are not with an approved broker, you will be required to move your holdings to an approved broker.

The Structured Finance Analytics Team is composed of a Quant team, a Data Analytics team, a Solutions team, and a Cashflow Modelling team. The Quant team has been growing over the last few years, comprising a global team of more than a dozen people today, located in the US and in Europe. The Quant team builds models and analytical tools to help rating analysts assess the credit risk of a transaction.