Data Scientist – Hybrid Toronto | Analytics for Growth

Heartland Coca-Cola Bottling Company, LLC; Toronto; Hybrid; CAD 83,000 - 105,000; Full time

Benefits offered by this job

Benefits package

Retirement program

Paid vacation & floaters

Volunteer opportunities

Job summary

Coke Canada Bottling is seeking a Data Scientist to join our Data Science and Advanced Analytics team in a hybrid capacity. You will tackle demand modeling, pricing elasticity, and optimization projects, translating complex data into scalable solutions for planning and execution across the organization.

You will collaborate with commercial strategy, sales, and operations to deliver practical analytics, build production-ready models, and communicate insights to both technical and non-technical

Qualifications

• Bachelor’s degree or college diploma in a quantitative field; Master’s is an asset.
• 3+ years in data science, analytics, or machine learning with business experience.
• Experience with SQL and Python; ability to scale analyses to production.

Responsibilities

• Contribute to analytics initiatives using statistical and ML techniques for demand, pricing, and product optimization.
• Develop analytical models to understand drivers and elasticity for practical insights.
• Build tools to improve trade spend effectiveness and promotional performance.
• Enhance planning and execution with data-driven insights across commercial, sales, and ops.
• Create visualizations and translate data insights into actionable recommendations.

Skills

SQL

Python

Data Viz

Education

Bachelor's degree or college diploma

Master's degree asset

Tools

Databricks

Azure

Power BI

Tableau

Job description

Coke Canada Bottling is seeking a Data Scientist to join our Data Science and Advanced Analytics team in a hybrid capacity. You will tackle demand modeling, pricing elasticity, and optimization projects, translating complex data into scalable solutions for planning and execution across the organization.

You will collaborate with commercial strategy, sales, and operations to deliver practical analytics, build production-ready models, and communicate insights to both technical and non-technical