Senior Data Scientist, Credit Risk

A career as a Senior Data Scientist, Model Development and Risk Production in the Credit Risk Analytics team at National Bank means acting as a hands-on subject matter expert in credit risk modelling and economic capital (EC) analytics. This role allows you to have a positive impact on the organization through your expertise in quantitative modelling, risk measurement, economic capital estimation, AI and advanced data analysis. 

Your role 

• Develop and enhance Credit Economic Capital (EC) and ICAAP processes to support new product launch, new portfolio integration, data migration, and internal and external reporting requirement.
• Develop and enhance EC, ICAAP, and concentration risk models.
• Modernize EC, ICAAP, and other risk reporting processes with effective use of AI tools, improving efficiency, automation, and analytical capabilities.
• Manage and execute EC and ICAAP production and reporting processes to support monthly and quarterly risk measurement activities. 
• Perform in-depth analysis of capital results, identify key drivers of capital movements, and prepare clear and insightful materials for senior management, internal stakeholders, and regulators.
• Support model development, implementation, and back-testing across other credit risk areas, including IFRS 9 and stress testing.
• Collaborate with Finance, IT, Business, AIRB Model Development, Model Validation, and Internal Audit to support model development initiatives and analytical projects. 

Your team 

The Risk Management team brings together highly specialized professionals who work collaboratively to strengthen the Bank’s risk management practices and analytical capabilities. 

Within the Credit Risk Analytics group with 26 colleagues, you will be part of a specialized EC team and report to the Senior Director, Credit Risk Analytics. Based in Richmond Hill, Ontario, the group are responsible for model development, model implementation, and risk reporting across Capital, IFRS-9 and stress testing. The team stands out for its deep quantitative expertise, collaborative approach, and commitment to continuously improving risk measurement methodologies. 

We aim to offer you maximum flexibility to promote your quality of life. This includes a hybrid work environment and a flexible, adaptable schedule. 

The Bank values continuous development and internal mobility. Our personalized training programs, based on learning through action, allow you to master your role and develop new areas of expertise. Tools such as the Data Academy, language training, the Harvard Learning Center, and coaching and mentoring support are always available to you. 

Requirements 

• Hold a master's degree in a relevant field such as data science, financial engineering, statistics, mathematics, economics, computer science, or a related discipline. 
• Have 3 to 5 years of experience in risk management, credit risk analytics, risk modelling, risk reporting, or a related quantitative discipline within a financial institution. 
• Demonstrate strong programming capabilities using tools such as Python, SQL, SAS, or other analytical programming languages. 
• Demonstrate ability to effectively and efficiently use AI tools to support quantitative modelling, risk analysis and reporting . 
• Possess advanced quantitative and statistical modelling expertise, with experience developing, validating, or supporting risk models. 
• Have experience working with economic capital models, stress testing, credit risk measurement frameworks, or related risk analytics methodologies. 
• Knowledge of Moody’s RiskFrontier platform or similar credit portfolio risk management solutions is considered an asset. 

Mandatory languages 

English 

French is considered an asset. 

Soft skills 

• Ability to learn quickly in a dynamic and evolving environment. 
• Adaptability and flexibility when responding to changing priorities and urgent business needs. 
• Strong analytical thinking and problem-solving skills. 
• Ability to manage multiple priorities while maintaining a high level of accuracy. 
• Collaborative mindset and willingness to contribute across teams. 
• Curiosity and openness to leveraging new technologies, including artificial intelligence tools, to improve efficiency.