Associate Director, Enterprise Model Risk Management

Job Description

Job Description What is your opportunity? As Associate Director, Enterprise Model Risk Management (EMRM) in our Group Risk Management (GRM) team, you will be responsible for end-to-end execution and documentation of credit risk model validation projects within RBC’s Canadian Banking platform. Your role is to independently assess and provide an objective opinion on the soundness of credit risk models using both qualitative and quantitative industry best practices. Specific activities include collaborating with model developers, opining on the adequacy of model documentation, assessing the suitability of models for their intended purpose, reviewing data inputs and outputs, building benchmark models to support validation conclusions, ensuring compliance with internal policies as well as regulatory requirements, communicating validation results, and providing recommendations for improvements. What will you do? Perform initial review and validation of newly developed credit models and make recommendations supporting use of the model. Employ various quantitative and qualitative techniques to review, test, replicate, challenge, benchmark and assess credit risk models. Utilize strong analytical and written communication skills to execute ongoing model validations once models reach their expected outcome period for all Canadian Banking credit risk models as governed by RBC’s Enterprise Model Risk Management policy. Develop comprehensive reports summarizing key observations, conclusions, and recommendations in support of completed model validations. Ensure model validations are planned and completed in accordance with timelines established in the Enterprise Model Risk policy based on each model’s materiality and uncertainty rating. What do you need to succeed? 3 years of model development or model validation experience, preferably related to credit risk models used within the financial services industry. Hands-on experience with artificial intelligence / machine learning modeling techniques (deep learning, XGboost) as well as logistic regression modeling techniques. Proficient Python programmer with a proven track record of delivering high-quality code. Comfortable working with large data sets. Solid understanding of data extraction and data mining, proficiency in SQL. Strategic thinker with superior interpersonal, verbal and written communication skills and with strong consensus-building skills. Post graduate degree in a quantitative field of study (i.e. PhD, Master of Mathematical Finance, Statistics, Computer Science, Applied Mathematics, Data Science or comparable). Nice-to-Haves: Knowledge of Canadian retail banking products and processes. A strong understanding of retail credit risk modeling theories, principles and industry best practices. A strong understanding of RBC’s policies, procedures, systems, risk appetite, risk tolerance, strategies and the overall role of risk management within RBC is a definite asset. Experience with Hadoop, Spark, objec

Required Skills