More jobs:
Data Scientist; Credit Risk Modelling
Job in
Regina, Saskatchewan, S4M, Canada
Listed on 2026-06-09
Listing for:
FCC / FAC
Full Time
position Listed on 2026-06-09
Job specializations:
-
IT/Tech
Data Analyst, Data Science Manager, Data Scientist -
Finance & Banking
Data Scientist
Job Description & How to Apply Below
Salary: $92,310 - $124,890 (plus performance based incentive)
Closing Date: 06/04/2026
Worker Type: Permanent
Language(s)
Required:
English
Benefits
Competitive total rewards packages: market‑aligned and performance‑based salary and incentive programs, flexible and comprehensive group benefit and savings plans, and well‑being support through benefits and wellness programs
Purpose‑driven work: building strong relationships, sharing knowledge and supporting the people who feed the world
Growth:
Learning and development opportunities to help you thrive
Hybrid work options
What You’ll Do
Design, develop, validate, and maintain credit risk models, including Internal Rating and Operational Credit Risk Models, Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD), and analyze how these components interact.
Unlock insights from complex internal and external data to support data‑driven decision‑making.
Apply advanced analytics techniques (e.g., statistical modeling, machine learning, forecasting) to identify risks and opportunities.
Translate complex analytical outputs into clear, practical insights for non‑technical stakeholders.
Collaborate with business leaders, technology teams, and external partners to implement analytics solutions in production.
Apply strong analytical judgment and rigor to reduce ambiguity and support strategic and operational decisions.
Required Qualifications
Bachelor’s degree in finance, economics, mathematics, statistics, actuarial science, computer science, agriculture, or a related field.
4+ years of experience in data science, analytics, or risk modeling, including experience in developing credit risk models.
Strong understanding of Internal Rating, PD, LGD, and EAD, and their role in credit risk measurement frameworks, including IFRS 9 and Economic Capital.
Proven experience building, validating, and interpreting statistical or predictive models.
Strong foundation in statistics, mathematics, and analytical problem‑solving.
Proficiency with tools such as SQL, SAS, R, Python, Power BI, or similar technologies.
Ability to communicate complex analytical concepts clearly and influence decision‑making.
Strong collaboration and communication skills in cross‑functional environments.
Strong written communication skills, with experience producing clear, high‑quality model development and validation documentation.
Preferred Qualifications
Experience working with large, complex datasets (structured and unstructured).
Exposure to cloud environments and analytics platforms (e.g. AWS).
Experience supporting domains such as risk management, marketing and pricing, economics, finance, or strategy.
Experience mentoring or providing technical leadership to other analytics professionals.
EEO Statement
Indigenous Peoples
Members of visible minority groups
Persons with disabilities
Women
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