Germany
Developed Python-based analytics pipelines for large-scale portfolio stress testing, including credit risk models (PD/LGD) and climate transition risk methodology, with direct impact on RWA and ECL reporting. -Developed climate risk methodology for transition and physical cost estimation, used in portfolio stress testing -Developed scalable LGD stress methodology for residential real estate portfolios -Built PD stress models using client-level data for portfolio credit risk analytics -Built large-scale data processing pipelines using Python (Dask), SQL, and Oracle -Calculated portfolio impact on RWA, Expected Loss, and ECL for management reporting -Presented methodology and results to director-level stakeholders
Worked closely with developers, strategists, and traders on fixed income pricing models, conducting independent analysis, testing, and implementation review across pricing models, funding curves, prudential valuations and fair value adjustments. -Built independent Python frameworks to replicate cashflows, test model assumptions statistically, and benchmark against alternative models -Reviewed model source code to verify consistency between implementation and documentation -Analysed and tested a range of fixed income pricing models across credit, funding curves, and fair value adjustments, including PDE-based models and AVA frameworks -Provided structured feedback on model methodology, assumptions, and documentation to developers and senior stakeholders