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
- Robust 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