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Job Description & How to Apply Below
Work Location:
Toronto, Ontario, Canada
Hours:
37.5
Line of Business:
Analytics, Insights, & Artificial Intelligence Pay Details:96, CADTD is committed to providing fair and equitable compensation opportunities to all colleagues. Growth opportunities and skill development are defining features of the colleague experience compensation policies and practices have been designed to allow colleagues to progress through the salary range over time as they progress in their role. The base pay actually offered may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational needs.
As a candidate, you are encouraged to ask compensation related questions and have an open dialogue with your recruiter who can provide you more specific details for this role.
Job Description:
Role Overview The Model Development (MD) team within Risk Management is responsible for developing, enhancing, and implementing credit risk methodologies across wholesale and commercial portfolios. This role focuses on non-retail credit risk, covering IFRS 9 allowance, stress testing (e.g., EWST), and regulatory capital models.
The successful candidate will lead the development of robust, forward-looking credit risk models and drive innovation in modeling approaches to address the unique characteristics and limitations of non-retail portfolios.
Key Responsibilities Design, develop, and enhance statistical and quantitative models for non-retail credit loss forecasting (PD, LGD, EAD), IFRS 9 allowance, and stress testing frameworks.
Advance methodologies to address non-retail modeling challenges, including data sparsity, low default portfolios, name concentration risk, expert judgment overlays, and portfolio heterogeneity.
Build scalable tools and analytical solutions to support model development, testing, and implementation.
Continuously improve model performance through rigorous validation, monitoring, and incorporation of emerging risk drivers.
Ensure models meet internal Model Risk Management standards, regulatory expectations, and data governance requirements.
Partner with stakeholders across Risk, Finance, Model Validation, and Technology to support model development, review, and implementation.
Communicate insights, model assumptions, and limitations clearly to senior management and regulators.
Qualifications & Skills Graduate degree in quantitative discipline (e.g., Financial Mathematics, Statistics, Economics).Solid experience in credit risk modeling with exposure to wholesale/non-retail portfolios.
Strong understanding of IFRS 9, stress testing, and regulatory capital frameworks (e.g., Basel).Proficiency in Python or equivalent programming languages for data analysis and model development.
Strong analytical and problem-solving skills with ability to work with complex, imperfect datasets.
Excellent written and verbal communication skills, with ability to engage senior stakeholders.
Differentiating Capabilities (Highly Valued)
Demonstrated ownership mindset and bias for action, with a track record of proactively identifying gaps and independently driving end-to-end solutions in complex, ambiguous environments.
Strong focus on AI-driven innovation in credit risk modeling, including evaluating and applying machine learning, generative AI, and advanced analytics techniques in a controlled, regulator-friendly manner.
Experience leveraging AI-enabled tools (e.g., Copilot, code assistants, automation frameworks) to accelerate model development, enhance productivity, and streamline documentation and testing workflows.
Ability to research, assess, and operationalize emerging AI capabilities, including identifying where these approaches are applicable in non-retail portfolios (e.g., feature engineering, segmentation, scenario modeling), while thoughtfully managing model risk and explainability constraints.
Proven ability to challenge legacy methodologies and introduce innovative, scalable solutions that balance sophistication, interpretability, and regulatory expectations in low-default, data-constrained environments.
Experience in leading quant teams
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