London Area, United Kingdom
Experienced Senior Quant Modeller with a demonstrated history of working in Investment Banking. Skilled in Statistics, Economics and Econometrics applied in Credit Risk, Market Risk, International Capital Requirements (IFRS 9, Basel III, CCAR), Asset Liability Management etc . Strong quantitative professional with studies focused in Statistics, Econometrics and Quantitative Finance from Sorbonne University (Paris), Humboldt (Berlin) and ENSAE ParisTech .
Support and lead the build, enhancement and deployment of decisioning models for Digital Banking program. Use machine learning and data science methods. Support communication with Change Delivery and IT functions. Communicate with regulators in order to explain the compliance of the risk systems with IRB and other regulatory requirements. Proactively identify business needs for improved risk measurement and accommodate requirements in an effective and efficient manner. Review and validate existing credit risk models to establish their fitness for purpose in the business and recommend improvements or remedial action. Liaise with model owners, Central Monitoring team, Risk Systems and IT (Software: Python, R, Anaconda Environment, SAS, Agile software).
Responsible for supporting the lead methodology team to define and build PD, EAD and LGD methodologies for Santander Global Corporate Banking (SGCB) portfolio that demonstrate compliance with IFRS 9 rules. Appropriate oversight of the model developments and appropriateness for UK portfolios to the satisfaction of internal and external stakeholders and working with the local model owner in specifying data requirements, as well as writing model documentation as part of the internal and external approval process. Fair Value model development and process mapping. Translating the results of the model(s) into business language such that all assumptions supporting the development, the structure and outputs of the model(s) are clearly understood. Defining interpretation of the guidance to senior stakeholders where required (as it relates to modelling) and helping consolidate feedback via the UK methodology leads in the UK and Madrid to the regulator via industry associations. Conducting Prudential Regulation Authority (PRA) and European Banking Authority (EBA) stress testing analysis using consistent methodologies, scenarios and key assumptions as mandated by respective regulatory authorities.
Primarily responsible for supporting the Wholesale Credit Risk team to define and build PD, EAD and LGD methodologies for Barclays Investment Banking wholesale portfolio that demonstrate compliance with IFRS 9 rules. Facilitating the implementation of a number of model development projects from inception through to delivery. Ensuring that the model output is sufficiently robust to withstand independent scrutiny and challenge by internal validation and internal and external auditors. Working closely with the model owners and ensuring that the model meets local and global governance standards and has been through the appropriate sign off criteria for the business unit prior to implementation. Supporting the model development as required and associated methodological documentation. Conducting PRA, EBA and FED's stress testing analysis and assessing their impact on bank's impairments and capital.
Management of interest rate risk and liquidity risk, modeling and predicting the option of prepayments on fixed rate mortgage-backed securities (MBS) and its impact on the bank liquidity results, calculation of interest rate risk indicators, developing hedging strategies using financial derivatives (swaps or cross-currency swaps). Developing scoring models and rating, working with big data, implementation of portfolio risk metrics and models.
Basel II risk parameters modelling (PD, LGD, EAD), forecast of the rating agencies severity, calculation of the distance to default using KMV models (Standard &Poor’s) and stochastic equations (Black and Scholes), estimation by logistic regression models and transition matrix comparison. Advanced IRB methodology, developing practical solutions for modelling risk and IRB models for low default portfolios, testing of different credit risk models.