Who we are: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. If such a workplace intrigues you, we hope you'll join us.About the role:Do you love diving into data, solving complex problems with tech, and figuring out what really drives credit risk and customer behaviour? Are you equally skilled at balancing deep analytical thinking with pragmatic execution, and always delivering on time? If you're ready to take your data science, advanced analytic skills, and knack for digging into the details to the next level, while leaving your mark on a rapidly growing organization, we want to hear from you!We are seeking a Data Scientist, Risk Analytics & Modeling, to work under the direction of the Director - Risk Analytics, Data Science & Reporting to develop and deliver robust risk machine learning and statistical models that support better credit decision-making, portfolio management, and risk measurement. You will play a key role in developing IFRS 9 models, behavioural models, and other predictive risk solutions, while building new modelling capabilities, operationalising and automating analytical processes, and turning complex data into actionable insights for stakeholders across the business.Building models that are technically sound, commercially useful, and scalable is no small feat and requires a ton of team-based continuous improvement — especially when there's always another process to automate, model to improve, or interesting dataset to play with.What you'll be doing:Build, validate, and refine sophisticated risk machine learning and statistical models across consumer credit, fraud, portfolio risk, and borrower behaviourDevelop and enhance IFRS 9 models, including expected credit loss, probability of default, loss given default, exposure at default, and related risk forecasting and segmentation methodologiesBuild behavioural and predictive models that leverage customer, transaction, portfolio,



and macroeconomic data to better understand and predict borrower behaviour and credit outcomesGo beyond the surface level to build sophisticated yet explainable models that connect data to real-world events, customer attitudes and behaviours to deliver insights and inform high-value business decisionsTranslate complex analytical problems into scalable modelling solutions, while balancing statistical rigour, interpretability, business impact, and practical implementationIdentifying high quality, well-managed data sources and opportunities to apply automation, data pipelines, machine learning, and AI to your workParticipate in strategic projects and partner with cross-functional teams to support resourcing for modelling and reportingDocument all models, methodologies, policies, strategies, data sets, and tools (using tools like Confluence) to ensure clarity and accurate internal transfer of knowledgeStay curious and experiment with new modelling techniques, analytical approaches, automation tools, and emerging technologies to continuously improve how we solve risk problemsWho you areYou bring an engineering mindset and look at risk modeling as a data science problem that needs to work well in, and adjust to, the real worldYou have strong data science, statistical modeling, data engineering, and automation skills, with a knack for translating complex borrower behaviours and risk dynamics into explainable predictions through data analysis, machine learning, and financial modellingYou know how to balance model complexity with simplicity, understand the trade-offs between performance, interpretability, stability, scalability, and implementation effort, and can clearly articulate and defend why a particular modeling approach is the right one for the problemElite critical thinking and problem-solving skills and the ability to clearly explain your work, assumptions, methodology, and conclusions to both technical and non-technical audiencesYou enjoy getting into the weeds, experimenting with recent approaches, automating repetitive work, and finding better ways to turn data into useful risk insightsBring energy, positivity, a critical eye, unimpeachable integrity, a team-first attitude, and a desire to contribute to building something great, togetherUniversity degree in Engineering, Math, Computer Science, Applied Math or Applied Sciences, Finance,



or another quantitative discipline0-3 years of full-time and/or internship/PEY experience in analytics, data science, risk management, finance, or a related quantitative fieldFoundational experience with Python, SQL, Excel, or similar analytical tools; experience building models, working with data, or automating processes is a strong assetExposure to machine learning, statistical modeling, credit risk, IFRS 9, behavioural modeling, or financial modeling would be a strong assetStrong pragmatic quantitative thinking, judgment, communication (verbal, visual, and written), with a demonstrated ability to break down complex problems and explain your workCurious and eager to learn, with the ability to dig into data, question assumptions, and develop an understanding of how models behave in the real worldHighly organized and driven, with the ability to manage competing priorities while meeting deadlinesA balanced sense of confidence and humility, with the ability to thrive in a collaborative environment and bring energy and enthusiasm to team cultureCapable of having fun while doing all of the above; we're serious about thisWinner of Canada's Most Admired Corporate Cultures, twice. We offer more than just the basics, take advantage of:An award-winning culture with a collaborative & inclusive team.Competitive pay and performance-based bonus:Annual Base Salary: $75,000 - $100,000Annual Bonus: 20%Committed to flexible work arrangements, offering 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.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. Please note that this posting is for an existing vacancy, and all employment offers are contingent upon a successful background and credit check, among other verifications.

📌 Data Scientist, Risk Modeling & Analytics (Toronto)
🏢 Financeit
📍 Toronto