Chief AI & Data Officer

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What The Market Looks Like

Chief AI & Data Officer roles remain scarce in Canada—we've tracked only 3 executive-level AI strategy postings across the country in the last six months. Demand concentrates in Ontario, Quebec, and British Columbia, particularly in Toronto and Montreal, with strong interest from technology, financial services, and professional services sectors. Compensation for this cohort typically ranges from $190,000 to $380,000 annually. The strongest candidates combine deep technical fluency in data architecture and machine learning with board-level communication skills, proven track records scaling AI initiatives across enterprise organizations, and the ability to translate technical capability into measurable business outcomes.

Job Responsibilities

Own the organization's AI and data strategy, from vision-setting through execution, ensuring alignment with business objectives and competitive positioning.
Lead cross-functional teams across data engineering, analytics, machine learning, and business to design and ship AI-driven products and capabilities.
Build and govern the data infrastructure, governance frameworks, and AI governance policies required to scale responsibly at enterprise scope.
Partner with the CEO and board to communicate progress, risk, and opportunity—translating technical work into strategic narrative for non-technical audiences.
Drive talent acquisition and retention for high-performing data and AI teams, including talent market intelligence and compensation benchmarking.
Establish metrics, dashboards, and accountability structures to measure AI initiative ROI and track progress against strategic milestones.
Integrate AI capabilities into existing business workflows and product roadmaps, balancing innovation velocity with operational risk.

Candidate Requirements

10+ years working in data, analytics, machine learning, or AI—with at least 5 years in a leadership or strategy role managing cross-functional, enterprise-scale initiatives.
Proven ability to build and scale high-performing data and AI teams from the ground up, including hiring, mentorship, and organizational design.
Deep technical foundation in data architecture, machine learning systems, and modern data stack—comfortable reviewing technical designs and pushing back on technical debt.
Track record translating AI and data strategy into measurable business outcomes—revenue uplift, cost reduction, efficiency gains, or risk mitigation.
Executive communication skills: board-ready presentations, ability to explain complex technical concepts to non-technical stakeholders, and comfort with investor/analyst relations.
Experience building or implementing AI governance, ethics frameworks, and responsible AI practices—increasingly critical at board level.