Risk Modelling Analyst - Digital Banking

Job Description

• Design, develop, and implement risk models such as application / behavioral / collection (ABC) scorecards, Expected Credit Loss (ECL) models, Interest Rate Risk in the Banking Book (IRRBB) models, Liquidity Gap Report, stress test models, etc.
• Utilize statistical and quantitative techniques, machine learning algorithms, and data analytics to enhance the predictive power and accuracy of risk models.
• Conduct thorough data analysis to identify key variables and market trends that impact credit, market and liquidity risks.
• Work closely with product managers and data owners to ensure the availability and quality of data required for model development.
• Code and implement risk models using programming languages such as Python, R, or other relevant languages.
• Collaborate with product managers and IT teams to integrate models into the organization's systems and ensure seamless execution.
• Perform ongoing validation and back-testing of risk models to ensure accuracy, relevance, and compliance with regulatory requirements.
• Identify and address any issues or weaknesses in the models through continuous monitoring and improvement processes.
• Prepare comprehensive documentation for risk models, methodologies, and validation processes.
• Ensure compliance with internal policies and regulatory guidelines.
• Collaborate with risk management, finance, and other relevant departments to understand business requirements and align risk models with organizational goals.
• Communicate model findings and recommendations effectively to both technical and non-technical stakeholders.

• Master's or Ph.D. in a quantitative field such as Statistics, Mathematics, Finance, or related discipline.
• At least 2-years’ experience in risk modeling within the financial industry.
• Experience with IFRS 9 accounting rules and ECL models is a plus.
• Strong proficiency in programming languages such as Python, R, or other relevant languages.
• Familiarity with machine learning techniques and statistical modeling.
• Excellent analytical and problem-solving skills.
• Knowledge of regulatory requirements related to risk modeling (e.g., Basel II/III).