Job description

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 works closely 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.

Description

Based in London, the trainee will be joining the quantitative research team of the XVA Desk for a period up to 12 months.

The intern's main mission consists in extracting valuable signals from electronic trading data using machine learning to:

. Detect patterns regarding customers trading behaviours

. Highlight relationships between customers hidden in historical data sets.

. Better serve our clients by providing more flexible trading and pricing options, whilst optimizing hedging and risk management strategies

The trainee will also be involved in research topics around the learning of pricing functionals with machine learning.

Objectives

. Identify behavioural clusters and develop interpretability tools to return analytical insights

. Leverage content and collaborative filtering recommendation system families, relying on embeddings' representation learning to build suggestions for sales advisory and trading axes use cases.

Requirements

. Strong quantitative skills (Mathematics & Finance)

. Good communication skills

. Hands on machine learning toolkit including Pytorch functionalities

. Good programming skills is mandatory (Python, C++)