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Specialist, Model Risk Oversight NEW
5 months ago
The Model Risk Oversight function in Enterprise Risk Management (ERM) provides oversight for the Model Risk Management framework through which the model risks across the organization are identified, measured, managed, monitored, and reported. The scope and depth of model oversight activities across the organization will be significantly increased to meet the requirements of the regulators (i.e., OSFI and FSRA) and the Definity's Model Risk Management Policy (MRMP) standards.
Reporting to the Manager, Model Risk Oversight, the incumbent is responsible for executing the model risk governance and oversight activities across the model lifecycle to ensure the model risk management initiatives comply with regulatory requirements and internal policies/standards/procedures. This position is a unique opportunity to learn how the models are developed and validated and how the model risks are controlled, mitigated, monitored and reported. As a member of Model Risk Oversight team and the Second Line of Defense, this role works closely with our business and control partners to manage the model risk exposure within our stated risk appetite, while allowing the business to achieve its goals. This role will support the management of model risk across the enterprise.
What can you expect in this role?
- Provide active support to oversee the model risk governance programs through metrics and reporting, resolve outstanding audit and regulatory issues, and respond to ad-hoc management requests. Ensure risk objectives are accomplished within approved timeframes and comply with regulatory requirements, internal policies, and standards, where applicable.
- Oversee the model inventory system and execute the attestation process to secure the change management procedure. Independent challenge the business to ensure model risks are appropriately evaluated and mitigation plans are progressing as planned.
- Represent ERM interests, and act as Model Risk Management subject matter expert, in the evaluation of new system or project initiatives. Perform the detailed quantitative and technical review (e.g., model validation, model performance monitoring, target review) as defined in the internal procedures or assigned by the management. Discuss, document, and effectively communicate reviews with the observations, conclusions, and findings to the management and key stakeholders.
- Develop and maintain positive and effective relationships with the key business stakeholders such that they view the Model Risk Oversight team as a valuable resource in effective risk mitigation.
- Drive the attestation process for the policy/standards compliance. Support the continued evolution of best practice model risk framework including standards, guidance, process, and templates. Challenge the business to implement MRM best practices and effectively designed controls as relates to model risks. Document and communicate compliance status to the management and key stakeholders.
- Contribute to design and execute model risk reporting (i.e., KRI) that leverage risk analytics, analyze and interpret the model information on the individual model and collectively from the model inventory system, and provide commentary insights to management and risk partners.
What do you bring to the role?
- University Degree in quantitative disciplines, e.g., Mathematics, Statistics, Actuarial Science, Data Science, Computer Science, Financial Mathematics/Engineering.
- Excellent knowledge and understanding of the technical modelling theory and validation testing techniques. A minimum of 5 years of experience in insurance/financial industry and experience in modelling or validation, including pricing/reserving models, capital/CAT/risk models, underwriting/claims/fraud analytics, etc.
- Industry certification or credentials will be an asset (e.g., FCAS/ACAS, CFA, FRM)
- Sound understanding of various modelling techniques, including but not limited to various regression models, decision trees, clustering/segmentation, GBM, random forest, and other AI/ML techniques. Comfortable conducting various tests. Proficient computing skills in Python, R, SQL, Excel (VBA), etc.
- Ability to manage multiple priorities efficiently. Thorough attention to detail. Ability to work independently or in teams. Excellent written and presentation skills to provide advice and explanation to various stakeholders.
