Specialist, Scoring and Modeling

The Specialist will join the Scoring and Modeling team, reporting to the Sr. Manager, Scoring and Modeling. The Specialist will lead and take ownership of model-related activities, including regular monitoring of existing scorecards, development, and implementation of new scorecards.

The ideal candidate will have in-depth practical experience in building predictive models using statistical methods such as logistic regression, machine learning model, as well as excellent data manipulation skills in SAS and other programming tools.

This is a hybrid-remote position. The selected candidate is expected to work from the office (London, Toronto, or Montreal) 1 day a week.

Primary Roles & Responsibilities:

•  Lead model development projects, with focus on credit risk scorecards, using various modeling techniques including Decision Trees, Logistic Regression and Machine Learning algorithms
• Utilize SAS and Excel to extract, manipulate and analyze raw data in order to provide value-added and timely analysis to assist in scoring related initiatives
• Develop and maintain comprehensive model documentation that adheres to regulatory standards
• Support operational model implementation by liaising with related parties to ensure the models are deployed accurately and promptly
• Develop, maintain, and review model monitoring and validation reports and provide insight into model performance with respect to a given business use case
• Take lead in liaising with external partners such as credit bureaus and modeling consultants, to ensure credit bureau data needs and analytic requirements are met
• Keep abreast of new statistical and machine learning techniques and tools, and industry best practices to predictive modeling
• Provide technical guidance, proactive support, and data-driven recommendations to management from predictive modeling perspective
• Effectively communicate and present key findings to both technical and non-technical internal business stakeholders leveraging visualization tools and techniques

Qualifications:

• 4+ years work experience in analytics and predictive modeling in a financial services industry, preferably in Credit Risk Analytics setting
• Bachelor’s Degree or Master’s degree in quantitative field such as statistics, economics, actuarial sciences, engineering, or mathematics
• Demonstrated understanding of credit risk model development, validation, and monitoring techniques
• Strong data science capabilities, with proven experience in manipulating data using SAS and SQL
• Strong knowledge of statistical techniques and concepts (regression, gradient boosting, neural networks, clustering, etc.) with the skill to determine the appropriateness of a technique for a given business problem
• Experience working with very large databases, data mining and/or data warehousing
• Expert skill level with Microsoft Excel pivot tables, index match functions and Microsoft Access or other database management software
• Experience with AWS data science and modeling tools such as AWS Sagemaker or AWS Forecast are considered an asset
• Strong communication skills (oral and written) and the ability to interact with all levels of the organization

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We are honored to be recognized for offering a best-in-class workplace, incentives, and initiatives to our dedicated employees who have helped build Fairstone into an employer of choice.

Learn more: https://www.fairstone.ca/en/about/canadian-lender

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If you’re seeking a role with a growing business that values employee development, Fairstone is the right place for you!

Fairstone is an equal opportunity employer. Accordingly, we will make reasonable accommodations to respond to the needs of people with disabilities. Individuals who view themselves as Aboriginals, members of visible minorities, and disabled are encouraged to apply in confidence.

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