Data Science Manager, Risk

Fig • Toronto, ON

6d • CA$95,000 - CA$120,000 • Hybrid

About The Position

Fig is an award-winning, high-growth Canadian FinTech modernizing the world of consumer credit. We provide simple, accessible and fully digital personal loans, removing the complexity and delays of traditional lending to better serve Canadians. Since launching in 2023, Fig has quickly built a strong reputation for innovation and customer trust. We have been named Consumer Lender of the Year by the Canadian Lenders Association and FinTech Startup of the Year by the FinTech Breakthrough Awards, and we are consistently recognized among Canada’s Best Workplaces. Our commitment to customers is reflected in our 4.8 out of 5 Trustpilot rating. Backed by Fairstone Bank of Canada and Ontario Teachers’ Pension Plan, Fig combines deep lending expertise with the agility of a startup. This foundation allows us to effectively meet the evolving credit needs of Canadians across a wide range of financial backgrounds.

The Role: Credit Risk Expert & Strategic Builder
We are looking for a hands-on, data-obsessed Data Science Manager, Risk to build and operationalize the models, data pipelines, and analytical frameworks that power our lending decisions. Reporting to the Director of Credit Risk, you will play a key role in advancing our credit risk capabilities through machine learning, data engineering, model governance, and data-driven experimentation. While this role does not include people management responsibilities in the short term, you will provide technical leadership by driving cross-functional initiatives, mentoring junior analysts, and championing best practices in data science and risk modeling.
You'll join an experienced team focused on Getting Stuff Done (#GSD), where curiosity, scientific rigor, and continuous innovation drive every decision. You'll work across the full model lifecycle from developing and deploying models to monitoring, governing, and continuously improving their performance. You should be comfortable navigating ambiguity, solving complex analytical problems, and translating insights into scalable, production-ready solutions.
This is an exciting opportunity to work across multiple data science disciplines, including credit risk model development, alternative labeling strategies, reject inference, model validation and quality assurance, feature engineering, model monitoring, production decisioning, and credit risk data engineering. You'll design robust data pipelines, improve model performance and governance, automate analytical workflows, and partner closely with Credit Strategy, Product, Finance, Growth and Engineering to deliver scalable, data-driven lending solutions. This is a newly created role, which means you’ll have the opportunity to help shape the mandate, build core processes, and make a visible impact as Fig continues to grow.
Culture matters deeply to us. You'll have the support of experienced colleagues across the organization who are passionate about solving challenging problems together. We're looking for someone who combines strong technical expertise with curiosity, collaborates effectively across teams, and thrives in an environment that values transparency, accountability, and continuous improvement.

Requirements

• 4 or more years of experience in Credit Risk management, modeling and/or related data analysis in a financial services, FinTech, lending and/or technology company.
• Strong proficiency in Python and SQL is required.
• Working knowledge of regression analysis, decision trees, loss forecasting, and statistical design of experiments.
• Exceptional Communication Skills: The ability to translate complex data into clear, professional narratives for Senior Management.
• Startup DNA: You thrive in fast-paced environments with limited structure and have a shared sense of purpose to "Get Stuff Done" (#GSD).

Responsibilities

• Develop Next-Generation Credit Models: Build, enhance, and deploy machine learning models for underwriting, reject inference, alternative labeling, and other credit risk applications.
• Drive Model Governance & Quality: Partner with model validation to ensure robust model governance through comprehensive documentation, performance monitoring, stability analysis, diagnostics, and ongoing model enhancements.
• Advance Credit Strategy: Partner with the Credit Risk team to evaluate underwriting policies, optimize risk segmentation, and translate model insights into data-driven credit strategies.
• Enable Scalable Credit Analytics: Develop analytical datasets, reusable feature frameworks, and scalable workflows that accelerate model development, portfolio monitoring, and strategic decision-making.
• Drive Statistical Experimentation: Design and evaluate statistically rigorous experiments to assess new models, features, and credit strategies, using data to quantify business impact and optimize decision-making.
• Productionize Decisioning: Translate analytical solutions into scalable production workflows, partnering with Engineering to automate credit decisioning and improve operational efficiency.
• Build AI-Powered Solutions: Develop and deploy AI-powered solutions across credit risk and fraud to enhance decision-making, strengthen fraud detection, and improve operational efficiency.
• Collaborate Across Teams: Partner closely with Credit Strategy, Product, Growth, Finance, Engineering, and Data teams to deliver high-quality, data-driven solutions that balance portfolio growth, risk, and customer experience.

Benefits

• Hybrid Work Environment: Balance of remote and in-office (currently one day a week in Toronto), without sacrificing the high-energy collaboration.
• Competitive compensation ($95,000 – $120,000 base + bonus).
• Retirement savings program with employer matching.
• Comprehensive medical, dental, and vision group insurance, as well as health and wellness spending accounts.
• Generous time off to help you recharge.
• Parental top-up to support your growing family.
• Continuing education stipend to support your professional development.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed