C# C++ Quantitative Analyst - Counterparty Credit Risk â€“ Investment Bank
Quantitative Analyst with Design and implementation of quantitative models, using C# or C++ in a source-controlled environment. Proven experience in a quantitative finance environment, preferably in a counterparty risk modelling capacity. (knowledge of asset simulation and stochastic models is a must); You must come from a strong academic background, with at minima a Masters in mathematics, physics or quantitative finance. Exposure to backtesting best practise in the context of counterparty risk. Practical knowledge of derivatives, their risk drivers and pricing models. Ability to contribute and operate with minimum level of supervision. This role will expose the candidate to a wide range of professionals within the bank. Accordingly he / she will also require good communication skills (both written and verbal) and the ability to work as part of a multi-disciplinary team.
Design and implement (or modify) counterparty credit risk backtesting methods. This will typically cover the development of a new backtesting technique or adaptation of existing techniques etc. and will also require a general understanding of the wider counterparty credit risk modelling framework to ensure compatibility. Accordingly, the role does require a solid quantitative background in counterparty credit risk. Working in close partnership with quantitative analysts, counterparty credit risk analysts and backtesting teams, the successful candidate will be expected to:
- Contribute to the delivery of the methodology projects, gathering and documenting requirements, considering all stakeholdersâ€™ interests, regulatory constraints and any potential deficiencies in the current methods exposed by
- quality assurance and regulatory processes;
- Investigate, analyse and design the risk methods, respecting the aims of accurately capturing risks whilst considering regulatory, system or other environmental constraints;
- Design, develop and test code changes required to implement the risk methods in the risk systems, whilst assisting the technical teams responsible for the production environment;
- Ensure the methods are adequately documented to support internal reviews and validation by internal auditors or regulators, by providing sufficient developmental evidences (i.e. materiality studies, description of assumptions,
- benchmarking against external methodologies and justification of methodological choices); take the lead in ensuring the successful review by model validation teams.
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