Senior Manager, Behavioral Analytics Model Validation

Knowledge

• 5+ years of experience in AML, fraud or marketing campaign model validation, model development or a related position; or equivalent experience in credit risk models
• Outstanding knowledge and relevant training in data mining, machine learning, and various statistical analysis techniques, with demonstrated experience pertaining to machine learning/statistical modeling.
• Experience in supervising or coaching junior team member(s)
• In-depth knowledge of industry best practices and understanding of AML regulatory requirements and the evolvement of in the regulations in AML, fraud and lending practices across jurisdictions.
• Good Knowledge of the bank’s different lines of business and various products offered.

• Analytical, detail-oriented, well-organized, highly self-motivated with good interpersonal skills
• Good ability of conflict-solving and to collaboratively work with first line model owner and developers.
• Proficiency in statistical and database modeling tools, particularly Python, SAS, R, SQL, Access/VBA, etc.
• Effective time management skills and multi-tasking skills to effectively deliver concurrent projects with completing timelines.
• Ability to supervise and lead junior team members to complete complex projects.
• Strong written and verbal communication skills; skilled at explaining complex concepts and processes in simple language

• An advanced degree (Maters, PhD) in a quantitative discipline, ideally in mathematics, statistics, computer science or engineering

Model validation (60%)

• Support the Director of Behavioral Analytics Model Validation in the development and execution of overall validation work plan
• Independently validate and/or guide junior analyst(s) to validate new and existing AML, Fraud and other customer behavioral analytics models with a concentration on models used for BMO Canada, US and international lines of business.
• Ensure the assigned validation projects are completed within required timeline and in compliance with the model risk management framework requirement.
• Provide effective challenge and communicate identified model risk to the first line model owners and developers to ensure the risk are well understood and mitigation actions are taken; escalating where necessary to senior management.
• Provide consultancy to the senior management and first line model owner and developer regarding the technical model issues of the AML and fraud models.
• Report writing and model inventory data attributes maintenance.
• Keep abreast of latest development in financial crime and AI/ML modeling techniques.

Managerial Leadership (25%)

• Host regulator update meeting on assigned projects to both MV senior management and the first line model owners and developers.
• Provide day-to-day supervision and training to the junior analyst, and review work completed by the reporting analyst before the draft report and recommendation are submitted to MV Director for approval
• Support the MV director to identify and implement improvement to work, process and systems to ensure operational effectiveness.
• Support various ad-hoc reporting requests from senior management, internal audit and regulators
• Manage HR administration requests of the junior analyst

Governance (15%)

• Assist the MV Director to ensure all models covered by the team meet the requirement of the Enterprise Model Risk Management Framework, e.g., all AML and fraud models used by the bank are validated and approved and issues identified are mitigated properly.
• Support the MV Director to ensure all validation review work is conducted in accordance to defined model validation process, and in compliance with all bank and regulatory requirements.

Performs validation of models and assesses model risk to confirm model appropriateness and capability for a designated portfolio. Provides effective challenge during model development and communicates decisions regarding model use to the business to ensure transparency and understanding of models and model risks. Assesses model’s capabilities, stress points and limitations; assesses the associated model risk and the controls in place to mitigate identified risks.

• Fosters a…