The Principal Structured Finance Quantitative Specialist will play a key role in supporting the Financial Institutions (FI) team in the structuring, risk assessment, and execution of significant risk transfer securitizations and other structured portfolio risk transactions.
Responsibilities:
Design, build, validate, and maintain internal asset and cash flow models to assess portfolio credit risk and tranche performance for SRT and other structured finance transactions (i.e. synthetic securitisations, cash ABS, credit-linked notes, warehousing, future flows)
Implement Monte Carlo simulations and other stochastic techniques to model portfolio losses, correlation structures, expected loss distributions, and tranche structural resilience
Develop and maintain infrastructure primarily in Python, process large datasets, integrating with SQL, Excel/VBA, and open-source libraries
Calibrate models using historical performance data, credit rating and correlation assumptions across asset classes (e.g., SME, corporate, consumer, trade receivables), ensuring alignment with regulatory and rating frameworks
Lead the quantitative risk workstream for structured finance transactions, providing expected/stressed loss analysis, credit enhancement sizing, tranche pricing support, and sensitivity analysis.
Support the structuring, credit risk, capital, and legal teams to ensure model outputs inform deal structuring, pricing, and internal approvals.
Prepare and present clear, rigorous documentation and presentations of model results for investment committees, risk committees, and senior management.
Ensure all models adhere to internal governance, validation, and audit standards, including periodic recalibration and documentation
Coordinate with model validation teams and external/internal auditors to defend modelling approaches and implement improvements
Present quantitative findings to internal stakeholders, including internal committees and senior management.
Requirements:
Strong quantitative skills in financial modeling and statistics/econometrics.
Advanced degree (MSc or PhD) in a quantitative discipline such as Mathematics, Engineering, Statistics, Physics, Computer Science, or Quantitative Finance
Significant practical experience in structured finance modelling, securitization analytics, or quantitative risk within a bank, asset manager, rating agency, or consultancy.
Proficient in Python, MATLAB, and/or C++, with experience building, validating, and maintaining large-scale asset and risk models
Expert knowledge of Monte Carlo simulation, credit curve construction, portfolio loss modelling, stress testing, default correlation, and cash flow modelling for structured products
Strong grasp of banking book risk concepts and credit analytics, such as Expected Credit Loss (ECL) models, PD/LGD modelling, credit enhancement analysis, recovery assumptions
Familiarity with regulatory capital frameworks (CRR, Basel III/IV, EBA SRT guidelines) and with IRB models and economic capital approaches for internal credit risk
Experience with structured finance analytics platforms (Intex, Moody’s SFW, Bloomberg SFLC) and database extraction and querying (SQL skills)
Experience with Quantic, risk pricing libraries, and sensitivity analysis tools desirable
Strong written and verbal communication skills, with the ability to present technical concepts clearly to non-technical audiences at all levels.
