Key Responsibilities: Conduct full end-to-end validations across credit risk, capital, finance, and fraud models. Develop and utilise challenger models to test current model performance and robustness. Provide independent technical challenge and assurance to modelling teams and stakeholders. Ensure regulatory compliance and strong model governance practices across all models. Job Experience and Skills Required: Education: Minimum: Degree in Mathematics, Statistics, Actuarial Science, or Data Science Preferred: Honours Degree in the above fields Experience: 36 years experience in scorecard development, validation, or monitoring Strong background in credit risk scorecards (application/behaviour) and predictive modelling Experience in data mining and analysis Comprehensive understanding of the credit lifecycle Skills: Advanced analytical and problem-solving skills Ability to collaborate effectively across business and technical teams High attention to detail and ability to work across varied model types Other Requirements: Clear criminal and credit record (non-negotiable) Apply now! For more exciting Finance vacancies, please visit: 👉

About The Role/Team: The Model Validation team provides assurance on the accuracy, robustness and governance of models. The unit plays a central role in supporting sound risk management, financial decision-making and regulatory compliance. Their work spans a variety of domains, including credit risk, finance, capital modelling, and fraud detection. The team collaborates closely with modelling and business units, offering technical challenge and validation expertise to ensure models remain fit-for-purpose in an evolving environment. Requirements: Proven 3-6 years experience in scorecard building, including developing, validating, and monitoring scorecards for credit risk application/behaviour models or marketing purposes. This experience will be used in the role to challenge models presented to the team and to build challenger models. Experience with data mining used for analyses and predictive modelling. Full understanding of the credit lifecycle. Strong analytical ability, with attention to detail and the ability to work across multiple model types. A collaborative mindset with the ability to engage effectively across technical and business teams. Education Minimum: Degree in Mathematics, Statistics, Actuarial Science or Data Science. Ideal or Preferred: Honours Degree in Data Science, Statistics, Mathematics or Actuarial Science. Conditions of Employment Clear criminal and credit record Should you not receive a response from us within one week of your application, your application has unfortunately not been successful.