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About the Role

Develop and deliver robust risk machine learning and statistical models to support credit decision-making, portfolio management, and risk measurement. Play a key role in developing IFRS 9 models, behavioral models, and other predictive risk solutions, while building new modeling capabilities, operationalizing and automating analytical processes, and turning complex data into actionable insights.

Responsibilities

Build, validate, and refine sophisticated risk machine learning and statistical models across consumer credit, fraud, portfolio risk, and borrower behaviour.
Develop and enhance IFRS 9 models, including expected credit loss, probability of default, loss given default, exposure at default, and related risk forecasting and segmentation methodologies.
Build behavioral and predictive models that leverage customer, transaction, portfolio, and macroeconomic data to better understand and predict borrower behaviour and credit outcomes.
Build sophisticated yet explainable models that connect data to real-world events, customer attitudes and behaviors to deliver insights and inform high-value business decisions.
Translate complex analytical problems into scalable modeling solutions, balancing statistical rigor, interpretability, business impact, and practical implementation.
Identify high quality, well-managed data sources and opportunities to apply automation, data pipelines, machine learning, and AI.
Participate in strategic projects and partner with cross-functional teams to support resourcing for modeling and reporting.
Document all models, methodologies, policies, strategies, data sets, and tools to ensure clarity and accurate internal transfer of knowledge.
Experiment with new modeling techniques, analytical approaches, automation tools, and emerging technologies to continuously improve risk problem solving.

Requirements

Engineering mindset with a focus on risk modeling as a data science problem that works in the real world.
Strong data science, statistical modeling, data engineering, and automation skills.
Ability to translate complex borrower behaviors and risk dynamics into explainable predictions through data analysis, machine learning, and financial modeling.
Ability to balance model complexity with simplicity, understand trade-offs between performance, interpretability, stability, scalability, and implementation effort.
Ability to clearly articulate and defend chosen modeling approaches.
Elite critical thinking and problem-solving skills.
Ability to clearly explain work, assumptions, methodology, and conclusions to both technical and non-technical audiences.
Enjoy experimenting with new approaches, automating repetitive work, and finding better ways to turn data into useful risk insights.
Bring energy, positivity, a critical eye, unimpeachable integrity, a team-first attitude, and a desire to contribute to building something great.
University degree in Engineering, Math, Computer Science, Applied Math or Applied Sciences, Finance, or another quantitative discipline.
0–2 years of full-time and/or internship/PEY experience in analytics, data science, risk management, finance, or a related quantitative field.
Foundational experience with Python, SQL, Excel, or similar analytical tools.
Experience building models, working with data, or automating processes is a strong asset.
Exposure to machine learning, statistical modeling, credit risk, IFRS 9, behavioral modeling, or financial modeling would be a strong asset.
Strong pragmatic quantitative thinking, judgment, communication (verbal, visual, and written).
Demonstrated ability to break down complex problems and explain work.
Curious and eager to learn, with the ability to dig into data, question assumptions, and develop an understanding of how models behave in the real world.
Highly organized and driven, with the ability to manage competing priorities while meeting deadlines.
Balanced sense of confidence and humility.
Ability to thrive in a collaborative environment and bring energy and enthusiasm to team culture.
Capable of having fun while working.

Skills

Data Science
Advanced Analytic Skills
Machine Learning
Statistical Modeling
Data Engineering
Automation
Financial Modeling
Python
SQL
Excel
IFRS 9 Modeling
Behavioral Modeling
Credit Risk Modeling
Risk Analytics
Data Analysis
Problem-Solving
Critical Thinking
Communication (Verbal, Visual, Written)

Location

Canada

Work Type

Hybrid
Full-time

Experience Level

0-2 years

Education Level

University degree in Engineering, Math, Computer Science, Applied Math or Applied Sciences, Finance, or another quantitative discipline

Salary/Compensations

$75,000 - $100,000
20% Annual Bonus

Benefits

Award-winning culture with a collaborative & inclusive team.
Competitive pay and performance-based bonus.
Hybrid workplace options.
Comprehensive medical, dental and vision coverage + Lifestyle Account.
RRSP Matching and Parental Leave Top UP Program.
In office massage, meditation & workout sessions.
Virtual events such as Lunch & Learns, company parties, fun team activities and charity initiatives.
Career learning and development programs.

About the Company

Financeit is a point-of-sale financing provider serving some of the largest home improvement and retail organizations in Canada.
Our platform helps businesses close more sales by offering customers affordable monthly payment options for their next big home improvement, vehicle or retail purchase.
We are small enough that you can make an impact within the company and large enough to make an impact in the market.
Financeit is a company where collaboration, inclusivity, fairness, and respect aren’t just ideas that get talked about, but are part of who we are.
Winner of Canada’s Most Admired Corporate Cultures, twice.

Equal Opportunity

Financeit is an equal opportunity employer. We celebrate diverse backgrounds and perspectives because we know they make our team stronger and our product better. We hire based on talent, potential, and culture add - no matter your background, identity, or life experience, you are welcome here.
We may use AI to support our hiring process. While these tools assist our team, applications are ultimately reviewed and assessed by our recruiters.
If you require accommodation at any stage of the recruitment process, please let our People Success team know.
All employment offers are contingent upon a successful background and credit check, among other verifications.