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12 Month Internship - XVA

🚀 Placement Program


AI generated summary

  • The candidate must have a minimum of a Bachelor's degree or equivalent, with experience in Computational Machine Learning engineering and XVA modelling. They should also have knowledge of AAD techniques and proficiency in programming languages such as C++, Python, and SQL. Strong soft skills such as creativity, autonomy, and team spirit are required.
  • The candidate will be responsible for mastering ML algorithms, implementing them in Pytorch, and collaborating with team members to develop high-quality financial models and C++ programming skills. They will also work on XVA implementation, propose solutions using neural networks, and demonstrate end-to-end understanding of applications. Efficiency, accuracy, reactivity, and innovation are key aspects of the role.

Placement Program

Software EngineeringLondon


  • 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


  • Minimum level of study:
  • Bachelor Degree / BSc Degree or equivalent
  • Training / Specialization:
  • Graduate
  • 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

Area of Responsibilities

Software Engineering


  • 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


Work type

Full time

Work mode