AIML Fraud and Compliance IVU

Role Overview: As an AIML Fraud and Compliance IVU at Barclays, you will lead the evolution of the digital landscape by utilizing cutting-edge technology to enhance digital offerings and deliver exceptional customer experiences. Your primary responsibility will be to manage model risk globally in alignment with the bank's risk appetite. You will work closely with the Group Chief Risk Officer to identify, assess, monitor, and manage model risk, ensuring decisions are based on accurate model outputs to mitigate adverse consequences. Key Responsibilities: - Validate and approve models for specific usages at inception and periodically - Validate models for their intended use and scope, considering complexity and materiality - Approval or rejection of models based on conceptual soundness, performance, and documentation clarity - Assess compensating controls to mitigate model risk - Document validation findings and recommendations in clear reports for model improvement - Evaluate model interactions and quality of Large Model Framework aggregate results - Design framework and methodology to measure and quantify model risk Qualifications Required: - Experience in Risk or other parts of Investment Banks, with the ability to understand and explain Risk and/or banking products - Experience in Model Monitoring and validation, with a preference for regulatory capital models - Strong communication skills to produce concise presentations for technical and non-technical audiences - Project Management skills to independently run projects and grasp PM concepts - Exposure to Data Science to understand, design, and challenge approaches Company Details: The Model Risk Management function at Barclays reports directly to the Group Chief Risk Officer and is responsible for independently managing model risk globally. The team's mandate is to ensure model risk is actively managed in line with the bank's risk appetite, driving innovation and excellence in the digital landscape. Location: This role is based in the Noida office. Role Overview: As an AIML Fraud and Compliance IVU at Barclays, you will lead the evolution of the digital landscape by utilizing cutting-edge technology to enhance digital offerings and deliver exceptional customer experiences. Your primary responsibility will be to manage model risk globally in alignment with the bank's risk appetite. You will work closely with the Group Chief Risk Officer to identify, assess, monitor, and manage model risk, ensuring decisions are based on accurate model outputs to mitigate adverse consequences. Key Responsibilities: - Validate and approve models for specific usages at inception and periodically - Validate models for their intended use and scope, considering complexity and materiality - Approval or rejection of models based on conceptual soundness, performance, and documentation clarity - Assess compensating controls to mitigate model risk - Document validation findings and recommendations in clear reports for model improvement - Evaluate model interactions and quality of Large Model Framework aggregate results - Design framework and methodology to measure and quantify model risk Qualifications Required: - Experience in Risk or other parts of Investment Banks, with the ability to understand and explain Risk and/or banking products - Experience in Model Monitoring and validation, with a preference for regulatory capital models - Strong communication skills to produce concise presentations for technical and non-technical audiences - Project Management skills to independently run projects and grasp PM concepts - Exposure to Data Science to understand, design, and challenge approaches Company Details: The Model Risk Management function at Barclays reports directly to the Group Chief Risk Officer and is responsible for independently managing model risk globally. The team's mandate is to ensure model risk is actively managed in line with the bank's risk appetite, driving innovation and excellence in the digital landscape. Location: This role is based in the Noida office.