Lead: Model Validation

About The Role/Team
The Model Validation team is a critical function within the organisation, responsible for ensuring the accuracy, reliability, and governance of models across credit, finance, and fraud risk domains. The unit provides oversight, technical challenge, and assurance to support sound decision-making and regulatory compliance. This role requires strong technical modelling capabilities, the ability to collaborate with a wide range of stakeholders, and the professional maturity to influence outcomes in a complex and evolving environment.

What Youll Be Doing

• Provide subject matter expertise, oversight and guidance with regards to the technical review and validation of models used in the organisation, particularly machine learning models.
• Ensure appropriate model governance and model risk management processes and standards are in place in order to manage model risk, improve and optimise model methodologies and usage.
• Lead and deliver independent validations across a range of models, including credit risk, operational optimisation, financial crime, and marketing campaigns.
• Provide technical oversight and mentorship to analysts, supporting skills development and professional growth.
• Design and implement challenger models using advanced machine learning methods.
• Research current and new machine learning algorithms to ensure proposed models are fit for purpose and market best practice is being followed.
• Ensure models meet governance, regulatory, and internal risk standards.
• Partner with model development teams and business stakeholders to provide effective challenge and assurance.
• Contribute to the ongoing enhancement of model validation methodologies, frameworks, and best practices.

Requirements

• 6+ years of proven experience in model development in the field of Data Science and ideally direct model validation or review experience.
• Strong technical expertise in tree-based methods, neural networks, clustering, anomaly detection, and other advanced statistical or machine learning techniques.
• Exposure to cloud platforms (AWS preferred) and emerging technologies such as large language models is desirable.
• Demonstrated ability to mentor and guide junior analysts, identifying skills gaps and supporting development.
• Excellent communication and stakeholder engagement skills, with the ability to work effectively across multiple teams and disciplines.
• Able to design and deliver end-to-end on all but the most complex ML projects without supervision.
• Data mining for use in analyses and predictive modelling.
• Have had own model(s) reviewed and validated or directly validated models in the past.
• Presenting and defending findings to model developers and senior management.

Education