Senior Manager, Model Development

Jobtailor; Toronto; On-site; CAD 90,000 - 130,000; Full time

Job summary

Jobtailor in Toronto, Canada, is seeking a quantitative risk professional to develop and validate credit risk models across AIRB, IFRS 9, ICAAP, and stress testing frameworks. You will collaborate with Risk, Finance, and IT teams to implement scalable analytics and robust data infrastructure.

The role requires a Master’s degree in a quantitative field and 2+ years’ experience in credit risk analytics, with strong Python and SQL skills, plus familiarity with Generative AI.

Qualifications

• Master's degree or higher in Statistics, Mathematics, Economics, Finance, Engineering, or related quantitative field.
• 2+ years of experience in credit risk model development, validation, or quantitative analytics in financial services.
• Strong knowledge of AIRB, IFRS 9, stress testing, ICAAP, or similar frameworks.
• Advanced programming skills in Python and SQL.
• Experience with statistical modeling and machine learning techniques.
• Familiarity with Generative AI and AI-assisted analytics is preferred.
• Strong documentation, presentation, and communication skills.
• Ability to independently manage complex analytical initiatives with minimal supervision.
• Candidates must pass criminal background and credit checks.

Responsibilities

• Develop, enhance, and monitor credit risk models and analytical solutions covering AIRB, IFRS 9, ICAAP, stress testing, climate risk, and portfolio risk management.
• Perform quantitative analysis, model calibration, validation support, annual reviews, monitoring, and risk assessments.
• Collaborate with Risk, Finance, Data, Technology, Audit, and Model Risk Management stakeholders.
• Contribute to robust data infrastructure for model development and analytics.
• Review data used in model development for required changes and improvements.
• Implement models and analyses for financial disclosure, regulatory reporting, and ad hoc analyses with IT and business units.
• Prepare model documentation, technical reports, and supporting materials for management, validation, audit, and regulatory reviews.
• Leverage AI, machine learning, and automation to improve model development, risk monitoring, reporting, and efficiency.
• Ensure compliance with internal policies, governance standards, and regulatory requirements.
• Lead complex analytical initiatives and deliver risk insights to senior management.

Skills

Python programming

SQL programming

Machine learning

Data science techniques

Statistical modeling

Generative AI

Technical documentation

Independent management

Communication skills

Education

Master's degree or higher in quantitative field

Tools

Generative AI tools

AI-assisted analytics

Process automation

Job description

• Develop, enhance, and monitor credit risk models and analytical solutions supporting AIRB, IFRS 9, ICAAP, stress testing, climate risk, and portfolio risk management activities.
• Perform quantitative analysis, model calibration, validation support, annual reviews, performance monitoring, and risk assessments.
• Collaborate with Risk, Finance, Data, Technology, Audit, and Model Risk Management stakeholders.
• Contribute to robust data infrastructure for model development and analytics.
• Review data used in model development for required changes and improvements.
• Work with IT and business unit teams to implement and execute models and analyses for financial disclosure, regulatory reporting, and ad hoc analyses.
• Prepare model documentation, technical reports, and supporting materials for management, validation, audit, and regulatory reviews.
• Leverage Artificial Intelligence, machine learning, and automation technologies to improve model development, risk monitoring, reporting, operational efficiency, and decision-making.
• Ensure model development and analytical activities comply with internal policies, governance standards, and regulatory requirements.
• Lead complex analytical initiatives, drive methodological enhancements, support regulatory and audit activities, and deliver risk insights to senior management.

Requirements

• Master's degree or higher in Statistics, Mathematics, Economics, Finance, Engineering, Data Science, Computer Science, or a related quantitative field.
• 2+ years of experience in credit risk model development, model validation, quantitative analytics, or related areas within financial services.
• Strong knowledge of AIRB, IFRS 9, stress testing, ICAAP, or similar risk management frameworks.
• Advanced programming skills in Python and SQL.
• Experience with statistical modeling, machine learning, and data science techniques.
• Familiarity with Generative AI, AI-assisted analytics, or process automation technologies is strongly preferred.
• Strong technical documentation, presentation, and communication skills.
• Ability to independently manage complex analytical initiatives and deliver high-quality results with minimal supervision.
• All candidates considered for hire must successfully complete a criminal background check and credit check.

Core Competencies

Demonstrates expertise in credit risk model development and validation, leveraging advanced programming skills in Python and SQL, while ensuring compliance with AIRB, IFRS 9, and ICAAP frameworks. Capable of leading complex analytical initiatives and collaborating effectively with cross-functional teams to deliver high-quality results.

Highest-signal resume keywords

• Credit Risk Model Development
• Model Validation
• Python Programming
• SQL Programming
• Statistical Modeling

ATS Optimization Keywords

Hard Skills

• Quantitative Analysis
• Model Calibration
• Performance Monitoring
• Risk Assessment
• Machine Learning
• Data Science Techniques
• Statistical Modeling
• Analytical Solutions
• Technical Documentation
• Regulatory Reporting

Soft Skills

• Communication Skills
• Presentation Skills
• Independent Management

Industry Keywords

• AIRB
• IFRS 9
• ICAAP
• Stress Testing
• Portfolio Risk Management
• Financial Services
• Model Risk Management
• Governance Standards
• Regulatory Requirements
• Data Infrastructure

Tools & Technologies

• Artificial Intelligence
• Generative AI
• AI-Assisted Analytics
• Process Automation