Senior Manager, Model Validation (Questbank)

Questrade Financial Group•Toronto, ON

2d•CA$140,000 - CA$180,000•Hybrid

About The Position

Questrade Financial Group (QFG) is seeking a highly motivated and experienced Senior Manager to lead the validation of AI/ML and credit risk models within its Risk Management department. This critical role will be responsible for independently validating a wide range of models, including those used for credit card and mortgage origination, account management, and Expected Credit Loss (ECL) calculations. The successful candidate will possess strong technical skills in model development, validation, and risk management, with a deep understanding of statistical modeling, machine learning algorithms, and regulatory requirements. They will also have excellent communication skills and the ability to effectively collaborate with model developers, business stakeholders, and senior management. QFG combines human-centric collaboration with AI-driven innovation to redefine financial services, and the ideal candidate will be a catalyst for change, using AI to transform and deliver unparalleled customer experiences and shaping a future where AI empowers our teams to do their best work. Join our diverse, inclusive, and hybrid workplace to unleash your creativity and nurture your curiosity without limits.

Requirements

Masters or PhD degree in a quantitative field such as Statistics, Mathematics, Econometrics, Financial Engineering, or Computer Science.
Minimum 7+ years of experience in model development, validation, or risk management within the financial services industry, with a focus on credit risk and/or AI/ML models.
Strong understanding of credit risk modeling techniques, including: Credit scoring and origination models, Account management models, Expected Credit Loss (ECL) models (IFRS 9).
Proficiency in statistical modeling and machine learning algorithms.
Experience with model validation methodologies and best practices.
Strong programming skills in statistical software packages such as R, Python, or SAS.
Excellent written and verbal communication skills, with the ability to effectively communicate complex technical concepts to both technical and non-technical audiences.
Strong analytical and problem-solving skills.
Ability to work independently and as part of a team.

Nice To Haves

Knowledge of Canadian regulatory requirements for financial institutions (e.g., OSFI E-23) is highly desirable.
Experience with credit card and mortgage portfolios is highly desirable.
Experience with cloud-based model development and deployment is an asset.

Responsibilities

Lead and execute independent validation of complex AI/ML and credit risk models, ensuring they are conceptually sound, fair, unbiased, statistically robust, and compliant with regulatory requirements (e.g., OSFI E-23).
Define the scope of model validation projects, develop validation plans, and manage projects to completion within established timelines and budgets.
Apply advanced statistical and machine learning techniques to assess model performance, identify potential weaknesses, and propose remediation strategies.
Enforce scientific rigor and ethical practices across all AI/ML initiatives.
Evaluate model risk, including conceptual soundness, data quality, implementation, and performance monitoring.
Prepare comprehensive validation reports documenting the validation process, findings, and recommendations.
Present validation results to model owners, business stakeholders, and senior management.
Effectively communicate validation findings and recommendations to model developers, business units, and senior management.
Collaborate with stakeholders to address model weaknesses and improve model performance.
Stay abreast of current and emerging regulatory requirements and industry best practices related to model risk management, particularly in the areas of AI/ML and credit risk.
Provide guidance and mentorship to junior model validators, fostering a culture of continuous learning and development.
Identify opportunities to improve the model validation process and enhance the effectiveness of model risk management.
Evaluate the quality and suitability of data used in model development and validation, ensuring adherence to data governance policies.