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Product Owner

We are committed to connecting Canadians through unique partnerships, our world-class network and content Canadians love—and our creative 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:The Manager, Data Science Product Owner is responsible for the Portfolio & Product Management, managing the roadmap, prioritization, and lifecycle of Machine Learning (ML) models and model feature-store capabilities.Acting as the primary interface between business stakeholders and the Model Development & Strategy (MDS) team, this role translates business opportunities and requirements into a prioritized portfolio of data science and data capabilities. The role works closely with Data Scientists, Data Engineers, MLOps teams, and business partners to ensure initiatives are properly defined, prioritized, delivered, adopted, and continuously optimized throughout their lifecycle.

This role requires a strong understanding of the end-to-end machine learning lifecycle and the ability to balance business value, technical feasibility, delivery effort, and available capacity across a complex portfolio of initiatives.

What you'll do:





- Own and maintain the roadmap and backlog for machine learning models and feature-store assets.
- Balance investment across new capability development, enhancement of existing assets, model retraining, and ongoing maintenance activities.
- Evaluate and prioritize competing requests based on business value, strategic objectives, delivery effort, dependencies, risks, and available capacity.
- Support portfolio planning activities and provide recommendations to Data Science leadership regarding priorities and sequencing.
- Business Partnership & Demand Management

- Act as the primary liaison between business stakeholders and the MDS organization.
- Gather and assess demand from business teams and translate business problems into clearly defined use cases, requirements, expected business value, success metrics, and implementation needs.
- Facilitate intake, discovery, and requirements discussions to ensure initiatives are appropriately defined before entering the delivery pipeline.
- Build strong relationships with stakeholders to understand evolving business priorities and identify opportunities for new analytical and machine learning capabilities.

- Delivery Coordination & Execution

- Coordinate end-to-end delivery across Data Science, Data Engineering, MLOps, and other teams responsible for developing and operationalizing solutions.




- Manage dependencies, delivery sequencing, risks, and cross-functional coordination across multiple concurrent initiatives.
- Partner with technical specialists to evaluate feasibility, implementation approaches, delivery effort, technical dependencies, and resource requirements.
- Facilitate planning, status reviews, and stakeholder updates to ensure successful execution and deployment.

- Model & Feature Store Lifecycle Management

- Maintain visibility into the inventory, status, and health of production models and feature-store capabilities.
- Track assets throughout their lifecycle, including intake, development, validation, deployment, monitoring, enhancement, retraining, and retirement.
- Support governance reviews, lifecycle checkpoints, and model portfolio management activities.
- Ensure appropriate documentation, traceability, and portfolio reporting are maintained.

- Adoption, Performance & Value Realization

- Track adoption, utilization, and business impact of delivered models and feature-store capabilities.
- Monitor asset performance and identify opportunities for optimization, reuse, consolidation, enhancement, or retirement.
- Support measurement and reporting of business outcomes and value realization.
- Prepare portfolio dashboards, performance reporting, and executive-ready summaries for leadership review.

What you bring:

- 3+ years working in Data Science or AI/ML, with experience in Product Management, Portfolio Management, or Program Management.
- Robust understanding of the machine learning lifecycle, including feature engineering, model development, validation, deployment, monitoring, retr

📌 Product Owner (Toronto)
🏢 Rogers communications
📍 Toronto