QA Developer

Role Title: Risk AI Model Developer Location: London About Quantitative Analytics Quantitative Analytics (QA) is a global organisation of highly specialized quantitative modellers and developers. QA is led by Olaf Springer, who is a member of Risk Exco. QA is responsible for developing, testing, implementing and supporting quantitative models for valuation and risk management of traded assets, regulatory and economic capital, impairments, scenario generation, credit and fraud risk decisions, surveillance modelling, asset-liability management, operational risk, net revenue and balance sheet forecasting, and stress testing across Barclays. About QA Market Risk The QA Market Risk team develops and supports all of the risk, capital and RWA models under the Market Risk umbrella of the Barclays investment bank. QA Market Risk is responsible for the full development lifecycle of the market risk models according to the standards defined by Basel, local governing regulators and internal risk management. The output of QA MR suite of models includes key capital and RWA metrics such as Value-at-Risk, Expected Shortfall, Incremental Risk Charge, CVA VaR, and standardized RWA. Market risk portfolio models span all asset classes in the trading book of the investment bank. In addition, QA Market Risk is responsible for all of RWA projections and GMS default loss measure under the FRB CCAR stress testing requirements. The team is located in London, New York, and Prague, and takes pride in its collaborative and geographically distributed profile. It is part of the QA Trading Cross-Product functional area. Overall purpose of role Lead Quantitative Model development in Risk AI area. Spearhead development of Risk models using new Machine Learning and AI based techniques using latest cutting technologies in LLM/GenAI space. Key Accountabilities

• Apply cutting-edge machine learning and artificial intelligence methodologies to enhance Market and Counterparty credit risk monitoring and management.
• Develop computational methods and mathematical and statistical models, including VaR/CVaR,stress-VaR to be used for risk management applications.
• Research, formulate, and implement quantitative models and solutions to optimize pricing and risk management of financial products across various asset classes, including: Interest Rates, Credit, Equity, Foreign Exchange, Commodities, Emerging Markets, and Counterparty Risk Trading.
• Perform computations and assess numerical implementations of analytical modules, models, and methodology documentation using mathematical theories and techniques including time series analysis, statistical analysis, and numerical analysis.
• Validate, formulate, and test quantitative pricing models to ensure adequacy.
• Implement and maintain analytics models and applications within Python library to generate analytical insight used to facilitate the development of market risk management.
• Build optimization tools using Python to facilitate counterparty risk management, including cash usage, balance sheet, liquidity, and regulatory capital.
• Define data requirements and perform theoretical modelling, empirical-testing, historical back testing, and statistical analysis of large data sets.