Product Owner

Role overview

Roadmap and demand management

• Own and maintain the roadmap and backlog for machine-learning models and feature-store assets.
• Balance investment across new development, enhancements, retraining, and maintenance; prioritize requests by value, strategy, effort, dependencies, risks, and capacity.
• Gather business demand and translate problems into use cases, requirements, expected value, success metrics, and implementation needs.
• Facilitate intake, discovery, and requirements discussions; recommend portfolio priorities and sequencing.

Delivery and lifecycle management

• Coordinate end-to-end delivery across Data Science, Data Engineering, MLOps, and other teams; manage dependencies, risks, sequencing, and cross-team coordination.
• Partner with technical specialists to evaluate feasibility, implementation approaches, effort, dependencies, and resource requirements.
• Track models and feature-store assets through intake, development, validation, deployment, monitoring, enhancement, retraining, and retirement.
• Support governance reviews and lifecycle checkpoints; maintain documentation, traceability, and portfolio reporting.

Adoption and value realization

• Track adoption, utilization, performance, and business impact of delivered models and feature-store capabilities.
• Identify opportunities for optimization, reuse, consolidation, enhancement, or retirement.
• Measure business outcomes and value realization; prepare portfolio dashboards, performance reports, and executive-ready summaries.

Required qualifications

• 3+ years working in Data Science or AI/ML, with experience in Product Management, Portfolio Management, or Program Management.
• Strong understanding of the machine-learning lifecycle, including feature engineering, model development, validation, deployment, monitoring, retraining, and retirement.
• Experience working with Data Scientists, Data Engineers, MLOps teams, and business stakeholders.
• Ability to manage competing priorities, coordinate cross-functional initiatives, and translate business requirements into actionable delivery plans.
• Strong analytical, communication, stakeholder management, and facilitation skills.

Preferred qualifications

• Experience supporting machine-learning, customer decisioning, marketing analytics, or AI product portfolios.
• Familiarity with feature stores, MLOps, model governance, and model lifecycle management.
• Experience with Databricks, Azure ML, Azure DevOps, Jira, or similar platforms.
• Telecommunications, marketing analytics, or customer analytics experience.

Schedule and employment

• Schedule is full time; the posting identifies this as a regular position with no applicable contract length.
• Day shift.

Workplace and travel

• Corporate employees are expected to work onsite at 1 Mount Pleasant, Toronto, Ontario.
• Travel requirements are up to 10%.

Background screening

• A Canadian criminal record check is required; a credit check and driver's abstract may be required depending on the role.

Benefits

• Benefits include health and dental coverage, pension, RRSP, TFSA, stock matching, paid time off for volunteering, and employee discounts.
• The posting also lists career development and wellness programs.