Manager, Data Science, AI Risk

Location: Toronto

Final date to receive applications:10/23/2026

Address:100 King Street West Job Family Group:

Audit, Risk & Compliance Build, operationalize, and continuously enhance the second-line AI Ongoing Monitoring and Independent Review program. The role is responsible for establishing sustainable oversight of AI performance, model and agent behavior, drift, emerging risk indicators, control effectiveness, and independent review activities across the AI lifecycle. The position supports remediation of audit findings, strengthens AI risk governance, and ensures alignment with evolving regulatory expectations.

Key Accountabilities AI Ongoing Monitoring Program Design, implement, and maintain the 2

LOD AI ongoing monitoring framework, methodology, standards, and reporting processes.

Establish key risk indicators (KRIs), key performance indicators (KPIs), thresholds, triggers, and escalation protocols for AI risk oversight.

Monitor AI solutions for performance degradation, drift, emerging risks, control effectiveness, and adherence to approved risk treatment plans.

Develop portfolio-level monitoring dashboards and reporting to provide transparency into AI risk exposures, trends, and emerging concerns.

Analyze monitoring results and provide independent challenge, recommendations, and escalation where required.

Independent Review Program Lead the development, standardization, and execution of independent review activities across AI use cases, and agentic AI solutions.

Develop review procedures, evidence standards, and quality assurance requirements for independent reviews.

Conduct independent assessments of AI governance, controls, monitoring activities, and compliance with applicable policies and standards.

Identify control weaknesses, process gaps, and emerging risks, and recommend corrective actions.

Track findings, management actions, and remediation progress through closure.

Risk Governance & Oversight Acts as a trusted advisor to business and technology stakeholders on AI risk monitoring and oversight practices.

Provides effective challenge on AI risk decisions, controls, risk assessments, monitoring activities, and risk acceptance decisions.

Supports the interpretation of emerging AI regulations, guidance, and industry best practices and assesses impacts to governance frameworks.

Represents AI Risk Management during audits, examinations, and independent reviews.

Supports the development and maintenance of AI risk policies, standards, procedures, and guidance.

Analytics, Reporting & Continuous Improvement Leads the monitoring, measurement, and reporting of AI risk program effectiveness to senior management and governance committees.

Identifies trends, systemic issues, and emerging risks across the AI portfolio and develops recommendations for management action.

Develops and maintains data repositories, dashboards, metrics, and reporting capabilities supporting AI oversight activities.

Uses data analytics and risk intelligence to continuously improve monitoring and review effectiveness.

Supports development of training materials and stakeholder education programs related to AI ongoing monitoring and independent review expectations.

Must-Have Capabilities Experience designing and operationalizing ongoing monitoring frameworks.

Experience developing and executing independent review, testing, validation, audit, or quality assurance programs.

Strong risk analytics and reporting capabilities, including dashboard and metric development.

Ability to identify emerging risk indicators, control weaknesses, and systemic risk themes.

Experience establishing governance processes, standards, methodologies, and evidence requirements.

Strong written and verbal communication skills, including executive and regulatory reporting.

Demonstrated ability to provide effective challenge and influence senior stakeholders.

Experience supporting regulatory examinations, audits, issue remediation, and closure validation.

Qualifications5-7+ years of experience in AI Risk, Model Risk, Technology Risk, Data Science Governance, Internal Audit, Validation, Independent Testing, or related fields.

Degree in Data Science, Computer Science, Engineering, Statistics, Mathematics, Information Technology, Business, or a related discipline.

Strong understanding of AI/ML lifecycle risks, monitoring techniques, model performance management, and emerging agentic AI risks.

Knowledge of AI governance frameworks and regulatory expectations (e.g., OSFI, OCC, Federal Reserve, NIST AI RMF, EU AI Act, SR 11-7…