Credit Risk Analyst Statistical Modelling in Banking Domain

We have an urgent requirement for Credit Risk Analyst – Statistical Modelling in Banking Domain of our banking client in Abu Dhabi (PWFH)

Strong knowledge and hands-on experience in credit risk assessment and analysis. Expertise in statistical modelling, including PD (Probability of Default)---Must and LGD (Loss Given Default) model development.--MustHands-on experience in Logistic Regression, Linear Regression, and Time Series Modelling.---MustStrong coding experience in SAS and Python for statistical analysis, data processing, and risk model development.--Must


Job SummaryWe are looking for a skilled Credit Risk Analyst with strong expertise in statistical modelling, credit risk analysis, and risk model development. The ideal candidate must have hands-on experience in Probability of Default (PD) and Loss Given Default (LGD) model development, regression techniques, time series modelling, and programming using SAS and Python.Key Responsibilities

• Perform credit risk analysis and develop statistical models to assess credit risk exposure.


• Develop, validate, and enhance Probability of Default (PD) and Loss Given Default (LGD) models.


• Apply statistical modelling techniques, including logistic regression and linear regression.


• Perform time series modelling and analysis to identify trends, patterns, and risk indicators.


• Use SAS and Python for data analysis, statistical modelling, model development, and automation.


• Analyse large datasets to identify risk patterns and support data-driven credit risk decisions.


• Support model performance monitoring, validation, and continuous improvement.


• Prepare analytical reports and communicate model results to relevant stakeholders.

 Skills Sets"

• Credit Risk Analysis – Strong knowledge and practical experience in credit risk assessment and analysis.


• Statistical Modelling – Solid understanding and hands-on experience in statistical model development.


• PD and LGD Model Development – Practical experience developing Probability of Default and Loss Given Default models.


• Logistic Regression and Linear Regression – Strong knowledge and hands-on application of both techniques.


• Time Series Modelling – Hands-on experience in time series modelling and statistical analysis.


• SAS Programming – Practical coding experience using SAS for data analysis and model development.


• Python Programming – Hands-on experience using Python for statistical analysis, modelling, and data processing.

Preferred Qualifications

• Bachelor’s or Master’s degree in Statistics, Mathematics, Economics, Finance, Data Science, or a related field.


• Experience in banking, financial services, or credit risk management.


• Knowledge of credit risk model validation, performance monitoring, and regulatory requirements is an added advantage.