Data Scientist; Credit Risk Modelling

Position: Data Scientist (Credit Risk Modelling )
Closing Date: 06/04/2026
Worker Type:
Permanent
Language(s)

Required:

English
Salary Range: $92,310 – $124,890 (plus performance‑based incentive)
We’re proud to be 100% invested in Canadian agriculture and food. As a federal Crown corporation, we provide financing, knowledge resources and business‑management software to over 103,000 customers nationwide.
Competitive total rewards package: 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 programmes.
Purpose‑driven work:
We build strong relationships, share knowledge and support 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 analyse how these components interact.
Unlock insights from complex internal and external data to support data‑driven decision‑making.
Apply advanced analytics techniques such as statistical modelling, machine learning, and 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 judgement and rigor to reduce ambiguity and support strategic and operational decisions.
What you’ll bring to the team   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 modelling, 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 IFRS9 and Economic Capital.
Proven experience building, validating, and interpreting statistical or predictive models.
Solid 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.
Not sure you meet every requirement? We encourage you to apply anyway.
EEO statement   Indigenous Peoples
Members of visible minority groups
Persons with disabilities
Women
Accessibility and accommodations:
To support an inclusive and accessible candidate experience, we encourage anyone needing an adjustment or accommodation during any stage of the recruitment process to email us at  An HR partner will respond and work with applicants who request a reasonable accommodation. Information received in relation to accommodation requests will not impact hiring decisions.

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