Associate Director, Enterprise Model Risk Management

RBC•Toronto, ON

5d•Onsite

About The Position

As an Associate Director / Senior Analyst, Enterprise Model Risk Management (EMRM) in our Group Risk Management (GRM) team, you will execute and document validations of the Bank’s enterprise-wide credit risk rating systems and methodologies, with focus on Wholesale and Retail credit risk systems, including acquisition & account management models, as well as Wholesale, Retail, and Margin Lending AIRB parameters (Probability of Default (“PD”), Loss given Default (“LGD”) and Exposure at Default (“EAD”)) used in both regulatory and economic capital. You will develop and implement tools and methodologies required to underpin credit risk systems and parameters validation, and provide insightful robust analyses of credit risk systems, acquisition & account management models and risk quantification validations.

Requirements

Model development or model validation experience, preferably related to credit risk models used within the financial services industry.
A strong understanding of credit risk modeling theories, principles and industry best practices.
Strong conceptual, analytical, detailed oriented and problem-solving skills
Strong computer skills – Python, SAS, SQL and Excel required; similar open-source programming languages (i.e. R, or Scala, PySpark) and code sharing solutions (Github) are essential
Solid understand various data system structures/processes and how they affect the inputs and outputs of credit risk data.
Comfortable working with large data sets
Experience with artificial intelligence / machine learning modeling techniques as well as logistic regression modeling techniques; experience with Python modules used in training deep learning models (e.g. torch).
Effective presentation and communication skills, with strong written capabilities essential.
Strong consensus-building skills.
Works well in teams
Execute with urgency while maintaining quality and efficiency; adapt to shifting priorities, coupled with a sense of urgency
Post graduate degree in a quantitative field of study (i.e. PhD, Master of Mathematical Finance, Statistics, Computer Science, Applied Mathematics, Econometrics, Engineering, Quantitative Finance, or a related quantitative field).

Nice To Haves

Ability to work in Unix, Teradata Data Warehouse and/or Hive Data Lake environments
Experience with Hadoop, Spark and similar data storage and processing tools.
Experience with deep learning methodologies.
Familiar with Tableau or other data visualization tools
Familiar with object-oriented programming concepts
Exposure to credit risk system design and OSFI’s CAR guideline is a definite asset
A strong understanding of RBC’s policies, procedures, systems, risk appetite, risk tolerance, strategies and the overall role of risk management within RBC is an asset.

Responsibilities

Perform ongoing Wholesale and Retail credit risk systems including acquisition & account management models as well as parameters validations and provide insightful analysis of validation results
Perform a wide range of data reconciliations and analyses, e.g. organizing, interpreting and analyzing data using various statistical techniques catered for validation purposes
Execute and document appropriate quantitative and qualitative tests, review of the logic and conceptual soundness of credit risk rating systems, acquisition & account management models, as well as parameters and their inputs, accuracy, sensitivity, back testing, benchmarking etc.
Develop and enhance approaches tailored to timelines and data availability, utilizing detailed or 80/20 solutions, and quantitative and/or qualitative approaches, as appropriate
Deliver validation findings and elicit feedback and remediation action plans / solutions from model stakeholders
Ensure project and risk objectives are accomplished within approved timeframes and complied with regulatory requirements, model risk policy and model operating standards