VP credit Risk & Analytics

VP Credit Risk & Analytics

Cambridge ON

With over $1Billion in loans funded, our client has helped hard-working Canadians with personalized money solutions with more flexibility than traditional banks across both its retail and digital channels. They are a member of the Canadian Consumer Finance Association, fully licensed lender with 110+ branches across Ontario. The company believes their customers deserve clear, understandable loan terms and therefore encourage fair and fully disclosed lending practices. The company is on a National growth path, working to digitize the end to end consumer lending experience.

About The Role:

The successful candidates will play an instrumental role assisting with the development of the company's strategy to enhance its Credit Scoring capabilities and exponentially grow its consumer loan portfolio, in branch and digital.

This role will support the development, enhancement and monitoring of all credit scores used in the entire lifecycle of credit: Acquisitions, Adjudication, Credit Limit Assignment, Portfolio Management, Collections, Fraud, Loss Forecasting.

The successful candidate will bring strong statistical knowledge and experience in a credit risk environment with a proven track record of working with other business leaders from across the organization to drive analytically based strategies.

Qualifications:

• 5+ years of experience within consumer lending environment
• Experience building predictive models, regression modeling, credit modelling for auto adjudication, decision trees, logistics regression, etc.
• Experience with R, Matlab, Python or Base SAS.
• Demonstrated understanding of credit scores and, their use in business strategies, post implementation use, and monitoring
• Knowledge of automated decision engines (helped automating decision engines for different clients)
• Strong project management and communication skills
• Proven analytical and conceptual thinker who can adapt to a rapidly changing environment
• Demonstrated ability in working within team and collaborating with multiple stakeholders to understand issues and solve problems
• Ability to distill and communicate complex analytic recommendations to both technical and non-technical stakeholders, both orally and in written presentations
• Educational backgrounds in Applied Statistics, Computer Science, Risk Management, Financial Engineering, Statistical Modelling