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Crédit Agricole CIB is the corporate and investment banking arm of Crédit Agricole Group, the 10th largest banking group worldwide in terms of balance sheet size (The Banker, July 2022). 8,600 employees in more than 30 countries across Europe, the Americas, Asia-Pacific, the Middle-East and North Africa, support the Bank's clients, meeting their financial needs throughout the world.
Crédit Agricole CIB offers its large corporate and institutional clients a range of products and services in capital market activities, investment banking, structured finance, commercial banking and international trade. The Bank is a pioneer in the area of climate finance, and is currently a market leader in this segment with a complete offer for all its clients.
Summary
The XVA/Scarce Resources team is part of the Global Market Division (GMD). This 30 people strong team entails 3 sub-teams:
- 1 trading team: based in Paris, London and Hong Kong in charge of pricing XVA and hedging to reduce PnL volatility.
- 1 Quants team based in London and Paris.
- 1 XVA Strategy Projects and Transformation team (XVA ST) based in London and Paris.
In the framework of major regulatory changes, the mandate of the team is to:
- Reinforce Bank risk management
- Help reach and maintain the right balance between Meeting accounting & regulatory constraints whilst remaining competitive
- Optimise scarce resources like Risk-Weighted Assets (RWA), Leverage Ratio…
- Manage defaults
The mandate of the quant team: is to produce quantitative modelling and innovative solutions for XVA, Counterpart Risk, Collateral and Credit topics. The quant team regularly interacts with a broad scope of internal clients:
- XVA and Scarce Resources desk for XVA pricing and modelling
- Risk department for Internal & Regulatory CCR, Accounting XVA, and SIMM
- Collateral desk for discounting, SIMM and IMVA with CCPs
- Trading and Risk Management for Credit derivatives
The quant team closely works with the business to study and assess the models’ behaviour and performance. It also plays a significant role in several strategic XVA and RWA projects by producing computational blocks using cutting-edge modelling and implementation techniques to ensure the bank can cope with the increasing list of regulatory measures (XVAVaR, SACCR, FRTB-CVA …) and metrics needed to manage our XVA reserves properly. As such, the quant team will be strongly involved in the Smart XVA Project.
The quant team continuously builds and upgrades XVA libraries and platforms to implement regulatory changes in an optimised architecture. The team is actively participating in developing the Collateral management platform for CCP and EMIR Initial Margin and working on various FO and Risk systems migration projects. Work on the XVA ML topics:
- Clients trading behaviour Analysis
Key responsibilities
- String ML skills:
- Master Supervised, Unsupervised and Reinforcement Learning algorithm
- Implement in Pytorch
- Thanks to close interaction with other team members, high Financial Modelling and C++ programming skills.
- Quickly master XVA implementation in the XVACCR Library.
- Assimilate the AAD methods recently implemented to compute XVA sensitivities to initial Market Data.
- Propose and discuss various solutions using Neural Networks to speed up XVA computation drastically
- Propose and discuss various solutions to use Neural Networks in E-trading and RWA optimisation.
- Demonstrate end-to-end understanding of applications (including, but not limited to, the ML algorithms) being created.
- Efficiency and accuracy of developments
- Reactivity in the function of supporting users
- Innovation in models and numerical techniques
Application criteria
Minimum level of study
- Bachelor Degree / BSc Degree or equivalent
Training/Specialization
- Experience Computational Machine Learning engineering
- Experience in XVA modelling
- Experience of AAD techniques
Soft skills
- Creativity, Autonomy, and Team spirit
IT tools
- XVA modelling
- AAD techniques
- Computational Machine Learning Engineering skills
- C++, Python, SQL programming skills
