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Our client, a leading financial institution within the Corporate Support division, is seeking two experienced Risk Analysts (Expert level) to join their Wholesale Credit Risk team on a 12-month contract. This role focuses on the development, monitoring, and enhancement of advanced predictive credit risk models that support Small Business, Commercial Banking, and Capital Markets portfolios. The successful candidates will work in a dynamic, data-driven environment, leveraging machine learning, artificial intelligence, and statistical modeling techniques to drive risk insights, reduce portfolio losses, and support strategic decision-making. This is a hybrid role based in Toronto, requiring 3–4 days onsite.
Job Responsibility:
Develop, implement, and monitor predictive credit risk models across commercial and wholesale portfolios
Work with large and complex datasets: extract, clean, validate, and analyze data from multiple sources
Perform feature engineering and support automation of credit modeling processes
Design and deploy machine learning and statistical models for credit scoring, surveillance, and risk assessment
Partner with stakeholders across adjudication and business lines to ensure models are aligned with business needs
Conduct data assessments, identify gaps, and support data procurement for modeling initiatives
Prepare comprehensive model documentation, code repositories, and reporting materials
Address and resolve findings from model validation, internal audit, and ongoing monitoring processes
Contribute to continuous improvement of modeling frameworks and risk analytics capabilities
Requirements:
Undergraduate degree in Computer Science, Finance, Mathematics, Statistics, Economics, or a related field
Minimum 5 years of experience in credit risk modeling or related quantitative roles
Strong programming skills in Python for model development and automation
Hands-on experience with large datasets, including data ingestion, transformation, and aggregation
Proficiency in SQL and big data/cloud technologies such as Hadoop, PySpark, and S3
Solid understanding of advanced statistical methods and machine learning techniques (classification and regression)
Experience with credit risk modeling and time series analysis
Familiarity with version control tools such as GitHub
Working knowledge of UNIX command line environments
Nice to have:
Master’s degree in a relevant field
Experience with IFRS9, stress testing, or capital modeling frameworks
Knowledge of GenAI applications in lending or risk analytics
Experience with additional programming languages such as R, Java, or SAS
What we offer:
Opportunity to work with a top-tier financial institution in a high-impact risk function
Exposure to cutting-edge technologies including machine learning, AI, and big data platforms
Collaborative environment with cross-functional stakeholders across business and risk teams
Potential for contract extension based on performance and business needs