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Join us as a Quant Analyst! QA Wholesale Credit Risk (WCR) develop models for capital (Basel), impairment (IFRS9), and stress testing (CCAR, PRA, EBA). Model outputs are utilised across a range of risk metrics, including RWA, pricing, Economic Capital, and for credit sanctioning. The model scope covers the Corporate and Investment Bank (CIB) within Barclays International (BI). You will support the development, calibration, monitoring and documentation of credit risk models in line with regulatory requirements, e.g. Basel, CRR, CCAR, IFRS9. You will enhance model management through automation and development of new approaches.
Job Responsibility:
Develop new credit risk models, contributing to the development through approval
Validate performance of new models
Document new models to required standards
Manage parts of complex projects, liaising with stakeholders to ensure project progress
Design analytics and modelling solutions to complex business problems using domain expertise
Collaboration with technology to specify any dependencies required for analytical solutions, such as data, development environments and tools
Development of high performing, comprehensively documented analytics and modelling solutions, demonstrating their efficacy to business users and independent validation teams
Implementation of analytics and models in accurate, stable, well-tested software and work with technology to operationalise them
Provision of ongoing support for the continued effectiveness of analytics and modelling solutions to users
Demonstrate conformance to all Barclays Enterprise Risk Management Policies, particularly Model Risk Policy
Ensure all development activities are undertaken within the defined control environment
Requirements:
Understanding the quantitative techniques used in developing and validating PD, LGD, and/or EAD models
Demonstrate ability to work in an environment where modelling decisions are regularly challenged
Post-graduate degree in a quantitative discipline, such as Statistics, Mathematics, Econometrics, Physics, Engineering, with experience of developing and applying statistical models within credit risk domain
Excellent knowledge of statistics, e.g. regression analysis, reject inference, decision trees, confusion matrix, cross-validation
Track record of producing high quality written communication including results of research and presentations for technical and non-technical audiences
Numerical programming ability using R and/or Python
Working experience with SQL
Experience in data visualization, cleaning, and feature extraction
Nice to have:
Experience with the Latex document preparation system
Familiar with continuous integration development framework (e.g. Teamcity), and source control (P4, Git, SVN)