Analytics Team Lead in Winnipeg

The Technical Lead, Advanced Analytics is responsible for delivering analytics and insights to support the ongoing enhancement and performance of a large loyalty program operated by a pharmacy retailer, as well as supporting initiatives related to the company’s SaaS products. This role reports to the Senior Manager, Analytics.

The ideal candidate will have hands-on experience with Python for data manipulation and analysis, proficiency in working with large datasets, and a strong understanding of database structures (SQL, Snowflake). Expertise in advanced analytics techniques such as predictive modeling (using machine learning) and segmentation, as well as business case development, test design and campaign analysis (including effectiveness and ROI), is essential. In addition, the candidate should have experience designing reports and other business intelligence tools, along with the ability to support and mentor team members in methodology design and review for deep-dive analytics projects. Experience with deploying machine learning models into production (automated training and scoring) would be desired.

This role presents a unique opportunity to join a dynamic team of analytical professionals who partner with both Business and Technology teams to design innovative, value‑adding customer engagement strategic solutions for clients. It is a great fit for motivated individuals looking to further develop their expertise in retail, customer behavior, and loyalty program analytics.

Primary Responsibilities

• Develop Custom Analytics Solutions: Create and deliver custom analytics solutions to drive strategic value in understanding customer behavior and campaign performance.
• Business Case Development & Test Design: Design and develop loyalty‑based financial modelling for loyalty program mechanics, product pricing scenarios, campaign performance estimates, etc.
• Campaign & Customer Segmentation Support: Assist clients in determining targeting criteria for promotional campaigns, customer segmentation, and conducting behavior change deep dives.
• Machine Learning Models and GenAI Solutions: Develop machine learning models and support implementation and automation into production and leverage LLMs where appropriate to support analysis.
• Lead & Oversee Analytics Methodologies: Design and support the execution of analytical methodologies to ensure consistency, accuracy, and innovation across all projects.
• Support SaaS Product Performance Analysis: Provide support for the company’s SaaS products by testing measurement methodologies, assisting in program performance reviews, and offering insights for continuous improvement.
• Reporting & Documentation: Design and develop standard and custom reports, and ensure thorough documentation is created.
• Ad‑hoc Analysis & Data Requests: Fulfill ad‑hoc analysis and data requests from clients, including gathering requirements, designing custom queries (Python, SQL), and conducting data integrity checks on large datasets.
• Communication of Insights: Share findings with peers in the Analytics team and externally to clients with clear visualization and impactful storytelling in presentations.
• Process Standardization & Automation: Contribute to process standardization, automation, and optimization leveraging GenAI to aid with productivity.
• Team Collaboration & Company Culture: Contribute to the team’s overall operations and the company culture by embodying the organization’s values and adhering to its policies.

Capability Requirements – Education, Skills & Experience

• Graduate degree in Statistics, Mathematics, or other quantitative fields; supplementary education in Business is a bonus.
• 5+ years of analytical, data mining, predictive modeling experience, preferably with some exposure to retail industry, and loyalty program performance analytics including campaign analytics, customer behavior analysis, etc.
• Familiarity with SaaS product performance metrics is a plus.
• Well versed with a solid understanding and experience of the structure of relational databases.
• Can create complex custom queries using Python, SQL or other data‑mining tools based on multiple data sources.
• Proven ability to design and develop financial models and business cases.
• Can demonstrate the use of GenAI to aid in overall productivity and/or enhance analysis.
• Ability to develop analytical frameworks and generate/communicate business insights.
• Strong attention to detail, high aptitude for problem solving and a natural interest in understanding and explaining consumer behavior/business.
• Excellent communication skills, both verbal and written and strong presentation skills.