Senior Model Risk Analyst

Job Requirements

About the Role

The Senior Model Risk Analyst in the Market Risk function under the Risk business unit is responsible for assessing and validating quantitative models used across Treasury, Wholesale, and Retail functions. These models, which support internal decision-making and product disbursement, are governed by the bank’s Model Risk Management framework. The role involves evaluating model performance, quantifying model risk, and ensuring alignment with industry best practices. The incumbent will also contribute to enhancing the model risk governance framework and communicating risk insights to senior management.

Key Responsibilities

Primary Responsibilities

• Conduct model validation using techniques such as econometrics, financial engineering, advanced statistics, machine learning, and data analysis.
• Evaluate and validate models across domains including Retail Analytics, Trading Risk, and Wholesale Credit Risk.
• Quantify model risk and prepare periodic reports to communicate the bank’s model risk status to senior management.
• Review and enhance the bank’s Model Risk Management framework in line with industry best practices.
• Maintain the bank’s model register and associated validation documentation to support the model risk governance process.
• Communicate effectively with senior management and business heads through clear and concise written and verbal updates.
  
  

• Recommend improvements to existing models to enhance business efficiency and decision-making.
• Stay updated with the latest trends in model development, maintenance, and risk management.
  
  

• Graduation: Bachelor’s degree in a quantitative discipline such as Engineering, Mathematics, Statistics, Engineering, Economics, or related fields
• Post-graduation: Master’s degree or MBA in Finance (preferred)
  
  

• Minimum of 2 years of relevant experience in model validation, development, or risk management, particularly in Retail, Wholesale, or Market Risk domains
  
  

• Strong expertise in quantitative modeling techniques and statistical tools
• Proficiency in programming languages such as Python, R, or SAS
• Deep understanding of model risk governance and regulatory expectations
• Excellent analytical, problem-solving, and documentation skills
• Strong communication skills for engaging with senior stakeholders
• Ability to manage multiple validation projects independently