Use SQL, Python, and statistics at CIBC to monitor fraud detection models in a hybrid Toronto work arrangement. Communicate findings clearly.
As a Data Scientist, Model Performance Monitoring and Reporting, you will support CIBC US Fraud within Advanced Analytics and AI. You will assess existing internal and vendor-developed models, identify performance changes, and report findings to business partners. The work connects statistical analysis with model development, evaluation, deployment, and monitoring across the lifecycle. You will also contribute to reliable processes, reusable code, and ongoing oversight requirements.
Key Responsibilities: Measure and track fraud detection model performance statistically
Investigate result changes and identify potential performance concerns
Prepare accurate,



timely reporting for business oversight
Communicate statistical findings to US Fraud partners
Contribute to automated monitoring pipelines and Databricks transition
Requirements: Experience querying, preparing, combining, and analyzing data with SQL
Experience writing accurate, readable, repeatable Python code
Solid statistical foundation for evaluating model performance
Understanding of machine learning model lifecycle stages
Degree or equivalent practical experience in a quantitative discipline
Apply Python, SQL, statistics, and model lifecycle knowledge to strengthen CIBC fraud monitoring and reporting.

📌 Fraud Model Monitoring Data Scientist (Toronto)
🏢 CIBC
📍 Toronto