Our Calgary based client requires an AI Product Owner. Candidates will be a Senior consultant with MS and SAP AI solution experience who will help drive successful outcomes on a number of AI initiatives surrounding the business.
Role: AI Product Owner
Start: June 2026
End: Dec 2026
Duration: Initial 6 month contract
Location: Hybrid prefer Calgary based but open to Canada-wide based consultants. Some ADHOC travel may be required. Prefer some availability to be onsite.
Job Description
Position Title: AI Product Owner - Business & IT Innovation Services
Position Summary: Reporting to the Director, Innovation & IT Services, the AI Product Owner is accountable for defining, prioritizing, and delivering AI-enabled capabilities that improve business outcomes and customer value across our digital platforms.
This role translates business needs into an actionable AI product roadmap, partners with stakeholders and delivery teams, and ensures responsible, secure, and compliant use of data and AI.
The successful candidate will balance experimentation and delivery, guide model and feature lifecycle decisions, and continuously measure performance to drive adoption and ongoing improvement.
Qualifications required for the position:
Education: Bachelor’s degree in Business, Computer Science, Data/Analytics, Information Technology, or a related field (or equivalent experience).
Experience: Minimum of 7–10 years of experience in Product Ownership/Management (or similar), with demonstrated delivery of data and/or AI-enabled products in production environments.
Past experience with customer service chat based AI solutions is a benefit.
Experience with MS and SAP Co-pilot/Joule AI solutions
Skills:
Strong understanding of AI/ML concepts and limitations (e.g., supervised learning, LLMs, evaluation metrics), with the ability to translate between business needs and technical implementation.
Strong understanding of Agile methodologies and Scrum framework; proven backlog management and prioritization skills.
Excellent communication, facilitation, and negotiation skills, including the ability to align diverse stakeholders around outcomes and trade-offs (value, risk, timeline, cost).
Experience working with cross-functional teams (data science, engineering, UX, QA, security, and operations) and with vendors/cloud AI platforms where applicable.
Strong analytical and problem-solving skills.
Knowledge of AI product delivery practices (model deployment and monitoring, data governance, privacy, and security considerations).
Key Responsibilities/Accountabilities:
AI Product Vision and Roadmap: Define and communicate the vision, outcomes, and roadmap for AI-enabled products and features aligned to business strategy.
Stakeholder Engagement and Discovery: Partner with business owners, customers, and operational teams to identify AI use cases, clarify problem statements, and define measurable success criteria. Act as the primary liaison between stakeholders and delivery teams.
Responsible AI and Governance: Ensure AI solutions are designed and delivered with appropriate governance, including privacy, security, risk, transparency, and human-in-the-loop considerations. Collaborate with legal/compliance and security as required.
Backlog Ownership: Create and maintain epics, user stories, and acceptance criteria for AI features, data requirements, and model capabilities. Ensure stories include quality, performance, and monitoring expectations.
Delivery and Value Realization: Drive execution with the Scrum Master, engineering, data, and QA teams to deliver AI capabilities in iterations. Validate outcomes post-release (adoption, accuracy, efficiency, revenue/cost impact) and adjust priorities accordingly.
Experimentation and Prioritization: Establish a discovery-to-delivery process (proof of concept, pilot, production). Prioritize initiatives based on impact, feasibility, data readiness, and risk. Align funding/budget and capacity with priorities.
Data and Model Lifecycle Partnership: Work with data owners and technical leads to define data sourcing, quality expectations, and model lifecycle needs (training, evaluation, deployment, drift detection, retraining). Support build/buy decisions and vendor evaluations where applicable.
Quality, Safety, and Testing: Partner with QA and engineering to define and execute testing strategies for AI (functional, bias/fairness where applicable, robustness, security, and regression). Ensure release readiness and rollback plans.
Monitoring and Continuous Improvement: Define and track product and model performance metrics (e.g., precision/recall, latency, cost, satisfaction, automation rate). Use insights to drive iterative improvements and communicate progress to stakeholders.
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