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 – Robust 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.

Skills: risk,credit,statistical

📌 Credit Risk Analyst – Statistical Modelling in Banking Domain (India)
🏢 TAT IT Technolgies
📍 India