Treasury Quantitative Analyst (AVP)
- Type: Permanent
- Location: England - London
- Industry: Financial Services - Banking
- Specialism: Treasury
- Salary: High Competitive aligned to the Quant sector
- Date Posted: 17/04/2025
- Consultant: Jack Woodlock
We are building a Treasury quantitative analytics team for a global banking group with HQ in London. Our mandate includes searches for Director, VP, and AVP level people to support Treasury Finance to manage Interest Rates Risk and Credit rate Risk of the banking book. This particular AVP level role will be responsible for developing statistical models for forecasting asset and liability behavioural balances
Role Description
- 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
Role Requirements
- Experience in developing quantitative behavioural models in Asset Liability & Management
- Deep understanding of statistical and econometric modelling techniques – e.g. time series analysis, regression models and various estimation techniques
- Excellent communication skills, including the ability to discuss technical matters with a non-technical audience as well as being proficient in python programming
- Previous experience in modelling non-maturing deposits, mortgage prepayment or mortgage completion models
- Strong experience in analysing large volumes of data including cleaning and subsequent pattern identification and clustering
- Experience developing, implementing of models which utilise more complex Machine learning techniques