Principal, Structured Finance Quantitative Specialist

Overview

Principal for Structured Finance Quantitative Specialist has the overall responsibility for the design, development, and maintenance of internal models to support investment evaluation and risk-based pricing of Significant Risk Transfer (SRT) transactions and other structured finance instruments within Banking (Financial Institutions team). This highly technical role focuses on modelling and analysing the credit quality and behaviour of underlying portfolios, with the objective of quantifying risk, assessing loss profiles, and supporting pricing and investment decisions from the perspective of an investor taking credit risk.

The role is focused on ensuring accurate pricing, tranche structuring, and credit enhancement sufficiency for investments. The role involves working at the intersection of quantitative modelling, deal structuring, and credit risk analytics. The Principal will act as the internal expert for asset modelling across portfolios and provide analytical support throughout the transaction lifecycle—from initial structuring to post-trade monitoring. The Principal is responsible for the design and delivery of technical training sessions for FI - EU Banks and Structured Finance team members, ensuring consistent understanding and application of structured finance risk analytics and modelling tools and acts as the main point of contact on all issues related to the development and design of structured finance quantitative risk measures.

• 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.
• Act as the internal quantitative subject matter expert for model-related questions during due diligence, execution, and post-trade monitoring.
• Provide guidance and mentorship to junior analysts and associates, strengthening the team's structured finance modelling and quantitative capabilities.
• Contribute to continuous improvement in structured finance analytics, modelling toolkits, and internal standard and workflows.
• Strong quantitative skills in financial modelling 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 Quant Lib, risk pricing libraries, and sensitivity analysis tools desirable.
• Strong written and verbal communication skills, with ability to present technical concepts clearly to…