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Job details

Job: XVA Quant Analyst – Machine Learning Engineer

Location: London, UK

Hybrid working: 3 days on-site

Employment type: Full Time

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:

• Help reach and maintain the right balance between Meeting accounting & regulatory constraints whilst remaining competitive

Key Responsibilities for the Computational Machine Learning Engineer

• Leverage close interaction with other team members to apply strong Financial Modelling and C++ programming skills.
• Quickly master XVA implementation in the XVACCR Library.
• Assimilate AAD methods to compute XVA sensitivities to initial Market Data.
• Propose and discuss various solutions using Neural Networks to speed up XVA computation.
• Implement and produce sensitivities specific to the Smart XVA project.
• Use a C++ API of a proven Machine Learning Library to implement a Neural Network:
• Ensure stability (training provides a solution) and good asymptotic behaviour
• Apply AAD to differentiate the Neural Network.
• Train the Neural Network using sensitivities produced for the project.
• Isolate issues to improve program effectiveness.
• All ML steps and components are implemented in a dedicated dll.
• Use trained Machine Learning to compute XVA.
• Propose and discuss various solutions to use Neural Networks in E-trading and RWA optimisation.
• Demonstrate end-to-end understanding of applications (including ML algorithms) being created.
• Interact with IT to industrialise various Smart XVA project outputs.
• Ensure efficiency and accuracy of developments
• Provide reactivity in supporting users
• Innovate in models and numerical techniques

Legal and Regulatory Responsibilities

• Comply with all applicable legal, regulatory and internal Compliance requirements, including, but not limited to, the London Compliance manual and the Financial Crime Policy.
• Maintain appropriate knowledge to ensure qualification to undertake the role. Complete all mandatory training as required to attain and maintain competence.

Key Contacts

• Other members of the XVA quant team
• XVA Trading and Strategy & Transformation Team
• IT

Qualifications/Education Required

• Master’s degree in computer science or engineering or equivalent experience

Experience Required

• Experience in Computational Machine Learning engineering
• Experience of AAD techniques
• Computational Machine Learning Engineering skills

Skills & Knowledge Requirements

• Creativity, Autonomy, and Team spirit

Who We Are

Quanteam Group is a Consulting firm specialized in the Capital Markets industry, with offices in Paris, London, Krakow, Brussels, New York and North Africa.

Since 2007, our 800 consultants provide major clients (Corporate & Investment Banks, Asset Managers, Hedge Funds, Brokers and Insurance Companies) with expertise in several projects such as Financial Engineering, Quantitative Research, Regulatory Implementation, IT Transformation & Innovation.

The firm mainly takes part in:

• Business consulting: Quantitative research, Risk management (e.g. Market risk, credit risk, counterparty risk), Banking regulations (e.g. Basel III, Solvency II, FATCA, EMIR, MiFID), Pricing & Valuation, Organizational Transformation & Process Improvement.
• IT & Information systems consulting: Business Analysis, Project Management, Change management, Front Office Support (functional and technical), Development (e.g. C++, Python, C#, Java, VBA), Financial Software (e.g. Sophis, Murex, Summit, Calypso), IT Transformation & Innovation.

As part of Quanteam Group, Quanteam UK & PL has today more than 80 consultants, working for major Capital Markets institutions in London and Krakow.