London, England, United Kingdom
• Extensive experience in finance as a quantitative analyst covering a wide range of models. Demonstrated success in designing innovative and sustainable models. • Econometrician and researcher with specialization in time series data analysis. • Passionate about machine learning, data science, AI and its applications. • Experienced in software development using Python within a production environment. • Strong project - and stakeholder management skills, being able to translate complex technical topics to stakeholders from diverse backgrounds. • Strong documentation skills, capable to produce documents that are senior management, audit and regulatory proof. • Curious mindset, always eager to learn and quick to adjust to new environments, tools and industries.
Development and implementation of risk models and analytics to monitor, assess, and manage trading risk across portfolios, ensuring alignment with the firm’s risk appetite and regulatory requirements.
- Designed and implemented “Capital Explain” dashboards in Python, with inter-active drill-down capabilities down to the trade level, for the front office to efficiently sign off on their RWA. The dashboards serve over 120 users since its recent go-live and reduced the sign off process from 2 months to 2 weeks. Reponsible for the codebase maintenance. As main user of the UI framework developed within my team, I took initiative to promote it, create training materials and help other Strats in utilizing the tool, which is now used by more than 20 developers. - Maintaining high code quality in the Kannon Strats Python library through peer code reviews, adhering to clean code principles, and implementing comprehensive unit tests. - Analyzed the impact of varying bump sizes on the Greeks of derivatives across all asset classes in order to close a regulatory finding. Took initiative to automate the previous control process, reducing the process from 6 weeks to a few days.
- Enhancing the methodology to allocate EC at the desk level. - Analysed Monte Carlo sensitivity-based P&L in comparison to full revaluation P&L for bonds, IR swaps, CDS, and EQ derivatives utilizing in-house pricing tools to advise upon changing the P&L methodology. - Implementation of a prototype of the full production EC model using Python.
- Developed the “Risk not in Exposure” methodology. - Developed the Initial Margin model. - Developed a method to calibrate the Stressed Exposure window taking into account risk factor volatilities as well as correlations assuming a Gaussian Copula model for the risk factor returns. I implemented the method in both Matlab and C++. - Main author of the CVA RWA methodology document for which I received a recognition award. - Took initiative to recalibrate the overly conservative CVA risk weights proposed in the BIS consultative papers. The results were published in the industry response letter coordinated by the ISDA.
Developed an innovative and efficient method for estimating multivariate, non-linear, non-Gaussian State Space models utilizing advanced importance sampling techniques, with an application to a Stochastic Volatility model.
- Responsible for the validation of credit risk models in the context of Basel II compliancy. - Selected to go on secondment in London to develop a Basel II awareness training for a major investment bank.
I was responsible for the validation of credit risk models in the context of Basel II compliancy.