Analyst - Risk Modelling/Credit Risk

Role Overview:

We are looking for a Credit Risk professional with strong experience in risk modelling, statistical analysis, and portfolio analytics. The role will be responsible for developing and monitoring credit risk models, conducting stress testing and scenario analysis, and providing analytical insights to support credit strategy and portfolio risk management.

Key Responsibilities:

• Develop, validate, and monitor credit risk models using SAS, SQL, and statistical modelling techniques to predict probability of default, loss, and other credit risk metrics.
• Perform credit risk analysis across portfolios and identify key drivers of portfolio performance and risk.
• Develop and enhance scorecards, loss forecasting models, and other credit risk analytics solutions.
• Conduct stress testing, scenario analysis, and sensitivity analysis to assess portfolio resilience under different economic and business conditions.
• Analyse model outputs and portfolio trends to identify emerging risks, opportunities, and areas for improvement.
• Provide data-driven insights and recommendations to senior stakeholders on credit risk, loss forecasting, scorecards, and portfolio performance.
• Collaborate with Credit, Risk, Business, Finance, Data, and Technology teams to design and implement effective credit risk strategies.
• Ensure models and analytical approaches comply with applicable regulatory and risk management requirements.
• Support model documentation, validation, performance monitoring, and periodic model reviews.
• Work with large datasets to ensure data quality, accuracy, and suitability for modelling and risk analysis.
• Stay updated on developments in credit risk modelling, statistical techniques, regulatory requirements, and emerging analytical methodologies.

Required Candidate Profile:

• 3 - 8 years of experience in Credit Risk Modelling, Credit Risk Analytics, Risk Analytics, or a related field.
• Strong hands-on experience in credit risk modelling and statistical modelling.
• Good understanding of Basel II/III regulatory requirements and credit risk frameworks.
• Experience with IFRS 9 and/or CECL/CCAR requirements, including expected credit loss and related risk modelling concepts.
• Proficiency in SAS, SQL, and at least one of Python or R.
• Strong understanding of scorecards, probability of default, loss forecasting, portfolio analytics, and model performance.
• Experience in stress testing and scenario analysis.
• Knowledge of machine learning algorithms and their application to credit risk modelling will be an added advantage.
• Strong analytical, problem-solving, communication, and stakeholder management skills.
• Experience in banking, NBFC, financial services, or credit analytics is preferred.