Senior Quantitative Risk Analyst

Canada Guaranty•Toronto, ON

2d•Hybrid

About The Position

Reporting to the Manager, Data Science and Risk Analytics, the ideal candidate will independently conduct quantitative analytics and initiatives supporting the firm's risk management and business objectives. The candidate will serve as a strategic partner to sales and other cross-functional units, designing advanced analytical frameworks that deliver the value-added insights needed to strengthen customer relationships. This role also involves preparing and presenting analytical findings that translate complex data into actionable recommendations for stakeholders across the organization.

Requirements

Undergraduate degree in a quantitative discipline i.e. applied mathematics, statistics, economics, finance, operations research or engineering.
Minimum two years of working experience in predictive/consumer/financial modeling, quantitative analysis, or reporting roles with the ability to synthesize large amounts of data and draw insights and recommendations.
Previous experience using SAS, SQL, Python, R, or other software for conducting quantitative analysis.
Practical statistical experience in an academic or business setting, including experience extracting, preparing, and analyzing large data sets.
Experience developing and presenting analytics in Excel.
Comfort working both independently and in teams.
Demonstrated ability to work efficiently and accurately.
Strong problem-solving skills.
Strong business acumen, with effective communication skills and comfort presenting to an Executive audience.

Nice To Haves

Graduate degree in a quantitative discipline strongly preferred.
Excellent programming skills.
Superior data management skills.

Responsibilities

Develop and maintain regular and ad-hoc analytical reports on key metrics and portfolio performance for lending partners.
Dive into large amounts of structured and unstructured data to extract information, gain insight and create value added information to be shared within organization. Preparing recurring data support to cross-functional teams and leading the enhancement of the current process.
Identify opportunities to enhance the risk management and support model monitoring through data-driven analytics.
Effectively communicate findings and insights to Executives and senior management in a concise and easy-to-understand way.