Ai jobs in Canada

As a Lead Data Scientist on the Fraud AI Model Governance team, you will oversee the AI Model Governance program for Fraud Data Science and AI (Fraud DSA). You will collaborate with model developers within Fraud DSA and external vendors to deliver high quality model risk governance documentation and model performance monitoring for Artificial Intelligence and Machine Learning models used by Fraud Management. You will also provide model development oversight for Fraud DSA to ensure model developers are complying with RBC Enterprise Model Risk Operating Standards.

• Lead the Fraud AI Model Governance team in developing and maintaining model performance monitoring for all Fraud models.
• Create appropriate metrics, set alert thresholds, identify and escalate model performance issues.
• As the leader of the centre of excellence for fraud ai model governance, collaborate with Model Development teams in Fraud DSA to deliver and maintain high quality model governance documentation that meets audit and regulatory expectations.
• Coordinate with Enterprise Model Risk partners and Fraud model developers to ensure model governance responsibilities are completed on time: Quarterly Model Performance Tracking, Annual Assessments, Issue closure memos etc.
• Provide Model Development Oversight for Fraud DSA. Ensure Fraud model developers are complying with RBC Enterprise Model Risk Operating Standards. Coordinate Fraud DSA’s responses to regulator and audit questions related to model risk.
• Collaborate with Fraud model developers to perform supplemental testing on Fraud models.

• 5+ years’ experience in advanced analytics, data science or AI related roles, working with large, real-world datasets.
• Experience with AI model risk governance documentation.
• Strong data analytics knowledge and expertise, with an understanding of common enterprise technologies and approaches including big data and cloud platforms.
• Professional oral and written communication skills, including ability to prepare technical documentation.
• Proven collaboration and partnership skills with the ability to navigate through ambiguity.
• Strong organization and time management skills and the ability to balance multiple deliverables with fixed due dates.
• Experience creating data visualizations with Tableau or similar tools.
• Experienced in using Python and Spark.

• Graduate Degree in a Quantitative Discipline.
• Fraud Data Science experience.
• Knowledge of Canadian Banking and payment industry.
• Experience working with transactional data.

We thrive on the challenge to be our best, progressive thinking to keep growing, and working together to deliver trusted advice to help our clients thrive and communities prosper. We care about each other, reaching our potential, making a difference to our communities, and achieving success that is mutual.

• A comprehensive Total Rewards Program.
• Leaders who support your development.
• Ability to make a difference and lasting impact.
• Opportunity to take on progressively greater accountabilities.

• Big Data Management
• Data Science
• Decision Making
• Machine Learning (ML)
• Natural Language Processing (NLP)
• Predictive Analytics
• Python (Programming Language)

Address: YORK MILLS CENTRE, 36 YORK MILLS RD:TORONTO

City: Toronto

Country: Canada

Work hours/week: 37.5

Employment Type: Full time

Platform: PERSONAL & COMMERCIAL BANKING

Job Type: Regular

Pay Type: Salaried

Posted Date: 2025-12-15

Application Deadline: 2026-01-09

Note: Applications will be accepted until 11:59 PM on the day prior to the application deadline date above.

At RBC, we believe an inclusive workplace that has diverse perspectives is core to our continued growth as one of the largest and most successful banks in the world. Maintaining a workplace where our employees feel supported to perform at their best, effectively collaborate, drive innovation, and grow professionally helps to bring our Purpose to life and create value for our clients and communities. RBC strives to deliver this through policies and programs intended to foster a workplace based on respect, belonging and opportunity for all.

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