Senior Data Scientist
FGL Sports
What the opportunity is
The Modeling & Segmentation Analytics Team at Canadian Tire Bank is responsible for developing and maintaining a robust suite of predictive models that support decisioning across our credit card customer lifecycle. We build risk scorecards and classification models used for acquisition, account management, and collections strategies, with a strong emphasis on analytical rigor, regulatory soundness, and business impact. As part of our continued growth, we are seeking a Senior Data Scientist who can play a key role in advancing our modeling capabilities, influencing consumer strategies, and delivering high‑quality, data-driven, machine learning models that inform responsible credit decisions. Reporting directly to the Manager of Data Modeling, you'll be working with a group of other data scientists in charge of the whole model development and monitoring process.What you'll do
Lead machine learning projects that drive origination, credit limit and collection strategies including managing delivery timelines and coordinating team resourcesOwn the full cycle scorecard design and development from initial planning and data sourcing to data modeling and validationDeploy new models and track day‑to‑day scoring activities, addressing system or procedural issues as neededMonitor effectiveness of all statistical models and recommend appropriate course of actionPerform optimization analysis and build supporting component predictionsCreate customer segmentation schemes that provide strategy teams with key customer insightsCollaborate closely with internal customers, retail‑side partners and subject matter experts to understand data, interpret results, translate business needs and synthesize solutionsDeliver effective presentations of key design elements and model build results to senior managementWork on related tasks and ad hoc activities to support business objectivesWho you are
A creative and courageous quick learner with the ability to navigate in an environment of change and ambiguity and help us take bold, strategic moves in a rapidly evolving and competitive marketplaceAction‑oriented, self‑starter comfortable taking calculated risks to better serve our customers and to support business objectivesOutcome‑focused, critical thinker with the ability to analyze and visualize, to ensure continuous improvement across our entire business Collaborative team player with excellent interpersonal skills, who builds relationships easily across various stakeholder groups to move initiatives forwardWhat you bring
University degree in Statistics, Actuarial Science or Mathematics. Postgraduate degree in a related discipline is a plusFive years or more experience with machine learning building predictive models, developing scorecards, performing statistical analysis, and segmenting customers. Exposure to credit risk management, credit card/banking or fraud is a plusSolid understanding of the steps required in model development and the principles of various modeling methodologies. Experience in WOE binning and logistic regression to build scorecards is a plusProficient in SQL, Python and SAS programming languages. Comfortable with ML processes like Pandas, XGBoost, scikit‑learn and LightGBM is a plusFamiliar with FICO Analytics Workbench, SAS Enterprise Miner, Knowledge Seeker, SPSS Modeler and other software ecosystems. Experience in Databricks is a plusDemonstrated superior analytical, mathematical, programming and problem‑solving skillsAbility to accurately and consistently understand and manipulate large volumes of granular and detailed dataProven ability to translate business needs into statistically sound solutionsCapable of clearly communicating analytical results to business stakeholdersAbout Canadian Tire Bank
Established in 2003 as part of Canadian Tire Corporation, Limited (CTC), Canadian Tire Bank is a Schedule I Canadian chartered bank and a core component of CTC's integrated retail and financial services model. We offer credit cards, savings, and investment products that are closely linked to our Triangle™ Rewards ecosystem, enabling Canadians to earn value through everyday purchases across banners under CTC's umbrella, including Canadian Tire, Mark's, SportChek, Atmosphere, PartSource, Gas+, Pro Hockey Life, Hockey Experts, Sports Experts and Party City. Serving customers through approximately 1,700 retail and gasoline outlets nationwide, Canadian Tire Bank is an integral part of CTC's True North strategy, guided by our unifying goal to make life in Canada better. Since 2005, Canadian Tire Jumpstart Charities has been helping kids overcome financial and accessibility barriers to sport and recreation in an effort to provide inclusive play for all kids of all abilities. At the heart of Canadian Tire Bank is our philosophy of building "Customers for Life." We are focused on delivering customer service excellence and value‑added products through a deep understanding
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The Modeling & Segmentation Analytics Team at Canadian Tire Bank is responsible for developing and maintaining a robust suite of predictive models that support decisioning across our credit card customer lifecycle. We build risk scorecards and classification models used for acquisition, account management, and collections strategies, with a strong emphasis on analytical rigor, regulatory soundness, and business impact. As part of our continued growth, we are seeking a Senior Data Scientist who can play a key role in advancing our modeling capabilities, influencing consumer strategies, and delivering high‑quality, data-driven, machine learning models that inform responsible credit decisions. Reporting directly to the Manager of Data Modeling, you'll be working with a group of other data scientists in charge of the whole model development and monitoring process.What you'll do
Lead machine learning projects that drive origination, credit limit and collection strategies including managing delivery timelines and coordinating team resourcesOwn the full cycle scorecard design and development from initial planning and data sourcing to data modeling and validationDeploy new models and track day‑to‑day scoring activities, addressing system or procedural issues as neededMonitor effectiveness of all statistical models and recommend appropriate course of actionPerform optimization analysis and build supporting component predictionsCreate customer segmentation schemes that provide strategy teams with key customer insightsCollaborate closely with internal customers, retail‑side partners and subject matter experts to understand data, interpret results, translate business needs and synthesize solutionsDeliver effective presentations of key design elements and model build results to senior managementWork on related tasks and ad hoc activities to support business objectivesWho you are
A creative and courageous quick learner with the ability to navigate in an environment of change and ambiguity and help us take bold, strategic moves in a rapidly evolving and competitive marketplaceAction‑oriented, self‑starter comfortable taking calculated risks to better serve our customers and to support business objectivesOutcome‑focused, critical thinker with the ability to analyze and visualize, to ensure continuous improvement across our entire business Collaborative team player with excellent interpersonal skills, who builds relationships easily across various stakeholder groups to move initiatives forwardWhat you bring
University degree in Statistics, Actuarial Science or Mathematics. Postgraduate degree in a related discipline is a plusFive years or more experience with machine learning building predictive models, developing scorecards, performing statistical analysis, and segmenting customers. Exposure to credit risk management, credit card/banking or fraud is a plusSolid understanding of the steps required in model development and the principles of various modeling methodologies. Experience in WOE binning and logistic regression to build scorecards is a plusProficient in SQL, Python and SAS programming languages. Comfortable with ML processes like Pandas, XGBoost, scikit‑learn and LightGBM is a plusFamiliar with FICO Analytics Workbench, SAS Enterprise Miner, Knowledge Seeker, SPSS Modeler and other software ecosystems. Experience in Databricks is a plusDemonstrated superior analytical, mathematical, programming and problem‑solving skillsAbility to accurately and consistently understand and manipulate large volumes of granular and detailed dataProven ability to translate business needs into statistically sound solutionsCapable of clearly communicating analytical results to business stakeholdersAbout Canadian Tire Bank
Established in 2003 as part of Canadian Tire Corporation, Limited (CTC), Canadian Tire Bank is a Schedule I Canadian chartered bank and a core component of CTC's integrated retail and financial services model. We offer credit cards, savings, and investment products that are closely linked to our Triangle™ Rewards ecosystem, enabling Canadians to earn value through everyday purchases across banners under CTC's umbrella, including Canadian Tire, Mark's, SportChek, Atmosphere, PartSource, Gas+, Pro Hockey Life, Hockey Experts, Sports Experts and Party City. Serving customers through approximately 1,700 retail and gasoline outlets nationwide, Canadian Tire Bank is an integral part of CTC's True North strategy, guided by our unifying goal to make life in Canada better. Since 2005, Canadian Tire Jumpstart Charities has been helping kids overcome financial and accessibility barriers to sport and recreation in an effort to provide inclusive play for all kids of all abilities. At the heart of Canadian Tire Bank is our philosophy of building "Customers for Life." We are focused on delivering customer service excellence and value‑added products through a deep understanding
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