Associate Director, Enterprise Model Risk Management

Perform initial review and validation of newly developed credit models and provide recommendations for their use.,Utilize various quantitative and qualitative techniques to review, test, replicate, challenge, benchmark, and assess credit risk models.,Execute ongoing model validations for Canadian Banking credit risk models, adhering to RBC’s Enterprise Model Risk Management policy, employing strong analytical and written communication skills.,Develop detailed reports summarizing key observations, conclusions, and recommendations from completed model validations.,Ensure model validations are planned and completed according to timelines established in the Enterprise Model Risk policy, considering each model's materiality and uncertainty rating.

3+ years of experience in model development or model validation, preferably with credit risk models in the financial services industry.,Hands-on experience with artificial intelligence/machine learning modeling techniques (e.g., deep learning, XGBoost) and logistic regression.,Proficient Python programmer with a proven track record of delivering high-quality code.,Comfortable working with large data sets, with a solid understanding of data extraction and data mining, and proficiency in SQL.,Strategic thinker with superior interpersonal, verbal, and written communication skills, and strong consensus-building abilities.,Postgraduate degree in a quantitative field of study (e.g., PhD, Master of Mathematical Finance, Statistics, Computer Science, Applied Mathematics, Data Science, or comparable).,Nice-to-Have: Knowledge of Canadian retail banking products and processes.,Nice-to-Have: Strong understanding of retail credit risk modeling theories, principles, and industry best practices.,Nice-to-Have: Strong understanding of RBC's policies, procedures, systems, risk appetite, risk tolerance, strategies, and the overall role of risk management within RBC.,Nice-to-Have: Experience with Hadoop, Spark, object storage solutions.,Nice-to-Have: Familiarity with Tableau or other data visualization tools.,Nice-to-Have: Experience with version control tools (git).

Post graduate degree (e.g., PhD, Master's) in a quantitative field