Quantitative Model Developer - IRB/Credit Risk Models

We are seeking a highly skilled Quantitative Model Developer to join our risk management team. The role focuses on developing, validating, and enhancing Internal Ratings-Based (IRB) and credit risk models to meet regulatory and business requirements. The successful candidate will leverage expertise in quantitative modeling, programming, and financial risk analysis to support credit risk measurement and reporting processes.

• Design and develop IRB and credit risk models, including Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD) models, in line with regulatory frameworks (e.g., Basel III).
• Perform data analysis, feature selection, and statistical modeling to enhance the predictive power and stability of risk models.
• Implement advanced statistical and machine learning techniques to improve model performance.

• Conduct backtesting, sensitivity analysis, and benchmarking to assess model accuracy and robustness.
• Document model development processes, assumptions, limitations, and validation results comprehensively for regulatory audits.

• Ensure models meet regulatory requirements such as PRA, ECB, or Basel standards.
• Prepare documentation and presentations for internal governance committees and regulatory submissions.

• Work closely with risk management, finance, and technology teams to integrate models into production systems.
• Partner with internal and external auditors during model reviews and stress testing exercises.

• Stay updated on emerging trends in risk modeling, machine learning, and regulatory changes.
• Drive innovation in modeling techniques and methodologies to enhance risk measurement frameworks.

• Master's or Ph.D. in Quantitative Finance, Statistics, Mathematics, Computer Science, Economics, or a related field.

• Proven experience in developing and validating IRB models or other credit risk models.
• Strong programming skills in Python, R, SAS, MATLAB, or similar statistical tools.
• Proficiency in handling large datasets using SQL, Hadoop, or other data engineering platforms.
• Knowledge of machine learning frameworks (e.g., TensorFlow, Scikit-learn) is a plus.

• Solid understanding of Basel frameworks, particularly IRB approaches and credit risk regulations.
• Experience in preparing regulatory submissions and managing regulatory reviews.

• Expertise in statistical modeling techniques, including logistic regression, Monte Carlo simulations, and Bayesian methods.
• Strong analytical mindset with the ability to handle complex problem-solving under tight deadlines.

• Excellent communication skills for presenting technical concepts to non-technical stakeholders.
• Strong organizational skills and the ability to manage multiple projects simultaneously.

• Experience working in a financial institution or consulting firm.
• Exposure to model risk management frameworks and best practices.
• Familiarity with cloud-based platforms for model development and deployment.