Select how often (in days) to receive an alert: Manager, Data Scientist

We are committed to connecting Canadians through unique partnerships, our world-class network and content Canadians love—and our innovative team is growing. We are looking for dedicated team members to join our Corporate team who have a genuine passion for making positive impacts on customers and the communities where we live and work. We have a variety of business units with exciting and meaningful work waiting for you, including Communications, HR, Legal and Corporate Affairs, Supply Chain, Finance, and Real Estate. If you are considering your next step, we have exciting opportunities waiting for you. Come build a rewarding career at Rogers and be a driving force behind our success story Who we're looking for:

We are looking for an experienced Data Scientist to join our team and help build, deploy, and optimize machine learning solutions that influence decisions across millions of customer interactions. Our team manages a large enterprise model portfolio supporting customer acquisition, retention, and lifecycle marketing. You'll work extensively within our Databricks and Azure based machine learning environment using Python, Spark. This is a highly hands-on individual contributor role for someone who enjoys owning the full model development lifecycle, from business problem definition and feature engineering to production deployment and performance monitoring. You'll work closely with business stakeholders, data engineers, and fellow data scientists to deliver measurable business value through advanced analytics and machine learning. What you'll do:

Build Production Machine Learning Models

Design, develop, validate, and deploy predictive models and machine learning solutions. Perform feature engineering,



model selection, hyperparameter tuning, and model evaluation. Monitor deployed model performance and recommend enhancements as business conditions evolve. Deliver models that are scalable, maintainable, and production-ready. Design and evaluate experiments, A/B tests, champion-challenger frameworks and model performance assessments to measure business impact.

Solve Business Problems

Partner with business stakeholders to identify, prioritize, and size opportunities where advanced analytics and machine learning can drive measurable business outcomes. Translate ambiguous business objectives into structured analytical approaches, clear success metrics, and actionable recommendations. Perform exploratory analysis to uncover customer behaviors, business trends, risks, and opportunities within large and complex datasets. Collaborate with stakeholders to define success criteria, quantify business impact, and ensure analytical solutions are adopted and utilized in decision-making processes. Present analytical findings, model results, and strategic recommendations to both technical and non-technical audiences in a transparent and compelling manner.

Work Across the Full ML Lifecycle

Develop reusable code, data assets, and model components. Collaborate with Data Engineering and MLOps teams on deployment and operationalization. Support model governance, documentation, validation,



and monitoring processes. Contribute to continuous improvement of data science best practices.

Collaborate and Innovate

Collaborate with stakeholders to define success metrics, quantify business impact and ensure analytical solutions drive measurable outcomes. Work with cross-functional teams including Marketing, Product, Technology, Data Engineering, and Analytics. Share knowledge and mentor junior team members when appropriate. Stay current with emerging machine learning and AI technologies and identify opportunities for practical application. Explore and apply Generative AI, large language models and emerging AI technologies where they provide practical business value.

What you bring:

Experience

5 to 8 years of experience in Data Science, Machine Learning, Applied AI, or Advanced Analytics. Experience developing propensity, churn, customer segmentation, recommendation, next-best-action, offer optimization models in production environments is highly desirable. Proven experience developing and deploying machine learning models in production environments. Experience solving real-world business problems using data and analytics.

Technical Skills

Strong programming skills in Python, SQL, and Spark for large-scale structured and unstructured data analysis and machine learning development. Experience using Git-based development workflows, including version control, branching strategies, code reviews, pull requests, and collaborative development practices. Hands-on experience with machine learning frameworks and libraries such as Scikit-Learn, XGBoost, LightGBM, TensorFlow, or PyTorch. Strong understanding of feature engineering, model evaluation, ex

📌 Manager, Data Scientist (Toronto)
🏢 Rogers communications
📍 Toronto