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.-- Must Hands-on experience in Logistic Regression, Linear Regression, and Time Series Modelling.--- Must Strong coding experience in SAS and Python for statistical analysis, data processing, and risk model development.-- Must Job Summary We 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.
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