Data Scientist (Credit Risk Modelling)

FCC / FAC•Kanata, ON

2d•CA$92,310 - CA$124,890•Hybrid

About The Position

As a Data Scientist, you’ll focus on building and improving tools to help FCC understand and manage credit risk. Working with complex data, you’ll design and validate analytical models that directly support risk management and data‑driven decision making. Your work will help assess, monitor, and mitigate credit risk throughout FCC, as models are implemented in real-world operations through close collaboration with business and technology teams. You’ll deliver clear, actionable insights that inform strategic and operational decisions and drive measurable improvements in portfolio risk outcomes.

Requirements

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

Nice To Haves

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.

Responsibilities

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.