25 York Street, Toronto, Ontario, M5J 2V5
President's Choice Bank is a wholly-owned subsidiary of Loblaw Companies Limited (LCL) and was established to promote President's Choice Financial services offered by strategic partners in all regions of Canada. PCF has grown to over 3 million customers earning millions in better interest rates, free groceries and other rewards. It operates in the following segments:
- Mastercard: The President's Choice Financial MasterCard is offered and operated by President's Choice Bank.
- Insurance: Home and auto insurance, travel and pet insurance are offered through affiliates and relationships with several insurance companies.
- PC Services, which includes PC Financial Insurance, PC Mobile, The Mobile Shop, Prepaid Cell Phones Express and gift card businesses, is a new business in growth mode.
- PC Optimum: All PCF customers have the opportunity to earn PC Optimum Points that can be redeemer in a LCL and SDM stores.
Loblaws Inc. is a wholly-owned subsidiary of Loblaw Companies Limited (LCL). LCL is Canada's largest food distributor and has expanded into certain non-food categories, with operations across the country. LCL concentrates on food retailing with the objective of providing consumers with the best in one-stop shopping for everyday household needs.
PCF is an important business unit within LCL that manages and develops the products and service programs in order to improve the customer experience, maximize value, and provide a platform to enhance customer insights and relationships. PCF's vision is to provide superior value financial products to consumers.
As a Predictive Modeler within the PC Financial Product Management team, the successful candidate will primarily develop predictive models for all lines of business within PC Financial. As a trusted Advisor this role will be accountable for building partnerships with key business stakeholders to provide valuable insights, analysis and key strategic information.
What You'll Do:
- Develop and implement statistical models for targeted cross-sell, acquisition and retention using machine learning techniques that include linear/logistic regression, decision trees and k-means clustering analysis
- Maintain statistical models that optimize CRM communications through all channels
- Track and monitor performance of models in production to determine degradation and the need for recalibration or redevelopment of models
- Explore large amounts of structured and unstructured data to extract information, gain insight and derive variables to be used as inputs into models
- Ensure models are accurate and supported by reliable data
- Document and track all model changes and document in an orderly fashion
What You'll Need:
- University Degree in applied statistics field (Operations, Research, Engineering, Mathematics or related).
- Experience working with machine learning algorithms including linear/logistic regression, decision trees, k-means clustering analysis
- Thorough knowledge of relational databases and data extraction/manipulation tools coupled with strong programming skills (SAS, SQL, R, Excel)
- Ability to take initiative, multi-task, work in a fast-paced environment, and able to learn independently
- Strong analytical and problem-solving skills
- Strong communication and presentation skills
- Experience in Credit Card industry
- Experience working with unstructured data in Hadoop environment
Come and join a winning team who demonstrates innovation, energy, creativity and vision. We recognize the importance of a diverse workforce and we therefor encourage applications from Aboriginal Peoples, women, members of a visible minority and persons with a disability. We thank all applicants for their interest, however, only those selected for an interview will be contacted.
Number of Openings:
PC Financial recognizes Canada's diversity as a source of national pride and strength. We have made it a priority to reflect our nation's evolving diversity in the products we sell, the people we hire, and the culture we create in our organization. Accommodation is available upon request for applicants and colleagues with disabilities.