Associate Director, Global Risk Analytics

RBC•Toronto, ON

17h•Onsite

About The Position

As part of the Group Risk Management team, the Associate Director, Global Risk Analytics is responsible for the conceptual design, development, and ongoing maintenance of the mathematical models used for the measurement and capitalization of the market risk and CCR of RBC's trading portfolio. This includes ensuring that the model's underlying methodologies are appropriate and that they are implemented with integrity, to facilitate the effective management of the bank's market risk, CCR, and capital adequacy assessment under CCAR scenarios.

Requirements

Broad knowledge of traded products across various asset classes, and knowledge of regulatory & internal risk management requirements for market risk, CCR and CCAR
Strong analytical and problem solving skills
Excellent programming skills (e.g., Python, MatLab)
Data management and analysis skills (SQL and Excel required)
Ability to work collaboratively to achieve team goals
Agility to adapt to changing circumstances in a dynamic environment
Strong English communication skills, both written and verbal, especially in the explanation of complex modeling concepts to senior management and regulators

Nice To Haves

Masters in Financial Engineering, or a degree in another quantitative subject such as physics, statistics, mathematics or mathematical finance and/or a relevant professional qualification, with concentration in quantitative methods and/or finance.
Knowledge in statistics is considered a strong asset.

Responsibilities

Work with model users to understand their business requirements.
Conduct research, review regulatory requirements and consult with industry stakeholders to evaluate best practices for modeling.
Make recommendations on model methodologies, and develop technical implementation, either for production usage or to serve as a prototype for benchmark testing.
Provide business requirements with technical implementation details and user acceptance criteria to technology teams for production deployment, and validate implementation using independently developed benchmark models.
Document model methodologies, implementation details and testing results, and work with internal validation to facilitate their approval of the models.
Develop tools to assess and monitor model performance, including assumptions and limitations, on an ongoing basis for reporting to the various model monitoring governance committees.
Investigate and remediate modeling issues identified through ongoing monitoring or by internal validation.
Re-assessment and testing of models, including assumptions and limitations and benchmarking against alternative models, and documentation of the results in models whitepapers and annual assessments for review by internal validation.