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The role: Barclays Risk In Risk Barclays develops, recommends, and implements controls and cost-effective approaches to minimise Barclay's risks, identifies and analyses potential sources of loss to minimise risk and estimate the potential financial consequences of an occurring loss. We’re looking for a Quant Analyst to join an expanding Applied AI team, focused on building and scaling internal machine learning models. This role will play a key part in developing sophisticated fraud detection capabilities and supporting the rebuild of rebuilds and refreshes of our internal models, driving real business impact across the organisation. You’ll work at the intersection of data science, model development, and governance, partnering closely with a range of stakeholders to ensure models are robust, scalable, and aligned to business needs.
Job Responsibility
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
Demonstrable Python programming experience
Proven background in Data Science
Strong experience with Machine Learning models
Nice to have
Experience with Spark or distributed data processing
Strong stakeholder management skills
Familiarity with Git and project management tools (e.g. JIRA)