About QA Counterparty Risk
The role is in the global Quantitative Analytics function, working in the Counterparty Credit Risk (CCR) team. The team is responsible for analytics for computing counterparty credit risk (CCR) for the bank’s derivatives portfolio. These are implemented in a shared C++ quantitative library which aims to reuse common high-
performance pricers across all use cases, requiring a high degree of collaboration with quants and quant developers across QA teams representing a wide-range of areas of the bank.
About Quantitative Analytics
Quantitative Analytics (QA) is a global organisation of highly specialized quantitative modellers and developers. 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.
Overall purpose of role
The purpose of the role will be to contribute to the development of models used in the CCR Monte Carlo engine for risk factor simulation, derivatives pricing, collateral handling and exposure calculation, for all counterparties of the bank and across all asset classes. The successful candidate will be expected to take ownership of several projects, being the main contact point and taking the lead on all aspects of the model development process, including research, model documentation, prototyping, implementation, code review, testing, and library release. This is a challenging role requiring excellent modelling and programming skills.
Key Accountabilities
Model development for Monte Carlo simulation, derivatives pricing, collateral handling and exposure calculation for Counterparty Risk.
Improvements to existing CCR models and methodologies.
Model documentation and testing.
Development of quantitative library functionality for counterparty risk.
Person Specification
Strong attention to detail and accuracy, ability to communicate on technical topics to a non specialist audience, effective time management, knowledge of financial models.
Essential Skills/Basic Qualifications
Qualification / Education Required
Master’s degree in a Computer Science, Mathematics, Physics or other scientific discipline from a top university.
Skills / Experience Required
Strong analytical and numerical skills.
Excellent problem solving skills.
Experience with Monte Carlo simulation with relevant modelling and statistical knowledge.
C++ and Python experience with strong algorithmic design and reasoning skills.
Desirable skills/Preferred Qualifications
Experience developing in large-scale libraries and development lifecycle.
