Organization Unit Purpose

Group Retail Risk is the Credit Policy, Credit Decision Analytics, and Collections Strategy unit for Retail Portfolios covering Personal Loans, Auto Loans, Credit Cards, Mortgages, Overdrafts and SME.

Credit Decision Analytics team is responsible for managing the credit risk of Emirates NBD Group Retail lending portfolio by providing analytical support. They are responsible for identifying and tracking key risk indicators and early warning signals; build, monitor and manage Risk models, scorecards, and strategies at an underwriting level and portfolio management level; identify risky pockets where Credit Risk is being observed. Responsible for managing end-to-end risk of the portfolio tracking model performance, suggesting improvements and implementing approved changes.

Job Purpose

Part of a team consists of Analysts / Statisticians / Model developers in devising, developing application and behavior scorecards, enhancing ad-hoc underwriting analytics and fraud analytics. Responsible for maintaining Retail Credit Risk models/ scorecards with the aim to deliver robust tools that are compliant with both internal and external regulations and provide business insights and recommendations.

Job Content

• Develop new/maintain existing application/behavior scorecards using internal and external data from credit bureau by leveraging advanced machine learning techniques such as logistic regression, decision trees, gradient boosting, neural networks etc.
• Perform feature engineering and build data pipelines to support modeling efforts using structured and unstructured data.
• Documenting model development process as per Bank’s standards.
• Develop and maintain a model inventory and ensure the inventory is complete, accurate, and consistent with the model governance policy.
• Support Model validation team with model documentation, queries related to model and subsequent model remediation.
• Analyze in score cut-off finalization, implementation and on-going monitoring.
• Monitor model performance and recalibrate as needed to maintain predictive power and rank order.
• Leverage advanced analytical capabilities of model data sciences to enrich scorecards and thereby performance.
• Track portfolio quality metrics in terms of early warning indicators and analyze huge volumes of credit cards transactions data for any potential fraud.
• Analytics support during the deployment of a model or a credit risk strategy in decision management system.
• Extend analytics support during POC engagements with external data providers and launch of new segments/products.
• Support with relevant data extraction and analysis to review IFRS 9 ECL projections.

Education

• 3–5+ years of experience in data science or risk analytics, preferably in retail banking, fintech, or consumer credit.
• Master’s degree in data science, Statistics, Computer Science, Economics, or a related quantitative field.

Technical Skills

• Good understanding of Machine Learning (ML) techniques such as Linear Regression, Logistic Regression, Decision Tree, Time Series, Cluster Analysis, Factor Analytics, Extract Gradient Boosting, Random Forest, Ensemble Learning, Support Vector Machines (SVMs), and Association Analysis, etc.
• Fair exposure to API infrastructure, Open APIs, LLMs, Gen AI concepts.
• Knowledge of retail asset banking products.
• Knowledge of model validation and monitoring techniques.
• Exposure to explainable AI (XAI) methods like SHAP or LIME.