Senior Specialist, Enterprise Risk Modelling & Insights

Modelling and Insights Capabilities

• Develop and maintain an advanced risk modelling and insights toolkit, comprising strategic tools, platforms, and methodologies tailored to various model types such as regression, classification, time series, clustering.
• Design, develop, and deploy risk models and generate actionable insights to support proactive, data‑driven decision‑making across portfolios.
• Collaborate with domain experts and Enterprise Risk teams to incorporate business‑as‑usual risk insights into dashboards, management reports, and risk assessments.
• Perform identification, scoping, and development of forward‑looking statistical and machine learning models that enhance enterprise risk monitoring and mitigation.
• Establish automated, repeatable pipelines for core risk model development, testing, and insight generation, ensuring consistency and scalability across Group‑wide applications.
• Ensure risk models are explainable, transparent, and compliant with internal governance and regulatory standards.
• Develop and maintain formal procedural documentation standardizing risk modelling activities, including data sampling, feature engineering, model selection, evaluation, and validation.
• Produce and maintain comprehensive documentation of risk models and insights initiatives, including development approach, validation results, performance monitoring, and implementation protocols in line with audit and governance requirements.

Infrastructure and Collaboration

• Build and maintain robust risk data and technology infrastructure to enable timely deployment of effective risk models and analytical solutions.
• Collaborate with the Group Analytics Centre of Excellence (CoE) and data engineering teams to ensure access to clean, well‑governed, and organised datasets.
• Promote modularity, reusability, and seamless integration of risk modelling assets into the Bank’s existing enterprise platforms, risk systems, and operational workflows.
• Contribute to the selection and adoption of tools and technologies that support efficient risk model lifecycle management and insight delivery such as Dataiku, Spark, Databricks.

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