A Brief Overview Performs understanding and validation of modeling methodologies and other risk management activities consistent with the Model Risk Management (MRM) framework and to comply with regulatory requirements. Conducts statistical processes to evaluate model conceptual soundness, assumptions, data quality and completeness, testing, validation, performance monitoring, and controls. The Quantitative Risk Analyst reports to the Model Risk Manager at the ERM and Operational Risk Department.What You’ll Need to Succeed:•Understanding of modeling assumptions, inputs, processing, development and model results.•Build and program challenger statistical models to test model owners’ determined monitoring framework. •Validate the adequacy of control processes that will mitigate future risk calculation errors.•Interact/Liaise with model owners, users, developers, and model reviewers.•Perform statistical based model validations, produce model validation reports, perform annual reviews, and analyzed on-going monitoring for all models+.•Workflow management to ensure deliverables are prepared according to their timelines.•Preparation and delivery of validation and engagement status updates to internal and external stakeholders.•Keep current with the best practices recommended by the bank regulators.•Interact/Liaise with regulatory bodies to support challenger models developed and utilized during validation exercises. •Performs any other special assignments assigned by the Model Risk Manager and/or the ERM and Operational Risk Director.•Monitors compliance with continued education requirements of the Bank and professional associations.•Proactively review current data entry processes for efficiency and identify process improvements, standard operating procedures, best practices.Minimum Requirements You’ll Need•Bachelor’s degree in Math, Economics, Finance, Statistics, Engineering or other quantitative disciplines is required. •2+ years working experience in risk management, statistical analysis, modeling, or other quantitative discipline.•Proficient in at least one programming language such as R, Python, SQL, or equivalent, with the ability to work with large datasets.•Microsoft Office knowledge in Word, Excel and Power Point.