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Added 11 mins ago
M.L.T. Charge - Quality
Ross Memorial Hospital
Kawartha Lakes, ON · Lindsay, ON
Full-time
Reporting to the a Integrated Director Laboratory Services, is responsible for the implementation, maintenance and improvement of the quality management system and Hospital Point of Care…
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Quality Manager
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Full-time
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$120,000 to $130,000 CAD / year
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Financeit

Data Scientist, Risk Modeling & Analytics

Toronto, ON, Canada

Pay: $75,000 to $100,000 CAD / year
Job type: Full-time
Apply by: Mar 30, 2027
Experience: 0-2 years of full-time and/or internship/PEY experience in analytics, data science, risk management, finance, or a related quantitative field
Posting language: English
Posted: Oct 1, 2026

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Original job description

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 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 behavioural and predictive models that leverage customer, transaction, portfolio, and macroeconomic data to better understand and predict borrower behaviour and credit outcomes
Go beyond the surface level to 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 modelling solutions, while balancing statistical rigour, interpretability, business impact, and practical implementation
Identifying high quality, well-managed data sources and opportunities to apply automation, data pipelines, machine learning, and AI to your work
Participate in strategic projects and partner with cross-functional teams to support resourcing for modelling and reporting
Document all models, methodologies, policies, strategies, data sets, and tools (using tools like Confluence) to ensure clarity and accurate internal transfer of knowledge
Stay curious and experiment with new modelling techniques, analytical approaches, automation tools, and emerging technologies to continuously improve how we solve risk problems

Who you are

You 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 world
You 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 modelling
You 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 problem
Elite critical thinking and problem-solving skills and the ability to clearly explain your work, assumptions, methodology, and conclusions to both technical and non-technical audiences
You enjoy getting into the weeds, 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, together

Requirements

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, behavioural modeling, or financial modeling would be a strong asset
Strong pragmatic quantitative thinking, judgment, communication (verbal, visual, and written), with a demonstrated ability to break down complex problems and explain your 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
A balanced sense of confidence and humility, with the ability to thrive in a collaborative environment and bring energy and enthusiasm to team culture
Capable of having fun while doing all of the above; we're serious about this

Benefits

Winner 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 - $100,000
Annual 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

Next Steps

If what you just read excites you, we'd like to hear from you! Please submit your application and we'll contact you if you are selected to move forward in the process.

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.

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More about this job

Responsibilities

Develop, validate, and improve machine learning and statistical models for credit risk, fraud, portfolio risk, borrower behavior, and IFRS 9 expected credit loss components. Build scalable and explainable analytical solutions, automate data and modeling processes, document methodologies, and communicate actionable insights to stakeholders and cross-functional teams.

Requirements

A degree in a quantitative discipline and 0–2 years of full-time or internship experience in analytics, data science, risk, finance, or a related field are required. Candidates should have foundational experience with Python, SQL, Excel, or similar tools, along with quantitative problem-solving, communication, organization, curiosity, and collaborative skills; exposure to machine learning, credit risk, IFRS 9, or financial modeling is an asset.

Skills

Python
SQL
Excel
Data Science
Statistical Modeling
Machine Learning
Credit Risk Modeling
IFRS 9 Modeling
Financial Modeling
Data Engineering
Data Automation
Predictive Modeling
Model Validation
Risk Forecasting
Data Analysis
Communication

Benefits

Competitive Pay
Performance-Based Bonus
Hybrid Workplace Options
Medical Coverage
Dental Coverage
Vision Coverage
Lifestyle Account
RRSP Matching
Parental Leave Top-Up
In-Office Massage
Meditation Sessions
Workout Sessions
Virtual Events
Career Learning And Development Programs

Visa sponsorship

Not detected in the job text

Keywords

Python
SQL
Excel
Data Science
Machine Learning
Statistical Modeling
Credit Risk
IFRS 9
Expected Credit Loss
Probability Of Default
Loss Given Default
Exposure At Default
Behavioral Modeling
Financial Modeling
Fraud Modeling
Portfolio Risk
Risk Forecasting
Predictive Analytics
Data Engineering
Data Pipelines
Automation
Model Validation
Model Interpretability
Macroeconomic Data
Customer Analytics
Confluence
Risk Measurement
Credit Decision-Making

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