Vice President, Data & Analytics

Key activities

• Define the enterprise data and analytics strategy (vision, drivers, and outcomes) in collaboration with the CIO, the Executive Leadership Team, and business domain leaders.
• Engage stakeholders across Supply Chain, Commercial, Finance, R&D, and Operations to validate priority use cases and translate them into data products with clear value cases and owners.
• Own the data and analytics portfolio: demand intake, prioritization, sequencing, capacity allocation, and retirement of low-value initiatives, anchored to enterprise outcomes.
• Establish the value realization framework for data and analytics initiatives, including benefits tracking, KPIs tied to business outcomes, and post-implementation reviews.
• Communicate the business impact of data and analytics initiatives to the Executive Leadership Team and Board of Directors.

Operating Model, Leadership & Transformation

• Establish the enterprise data and analytics function, including charter, operating model, RACI, and federated governance bodies.
• Build, lead, and develop a North American team across governance, MDM, data engineering, BI, analytics, and AI.
• Manage the function’s budget, vendor strategy, sourcing models, and KPIs.
• Lead the change agenda that supports adoption of new data practices, tools, and standards.
• Drive the development of a data-driven culture across Lassonde, including skills, behaviors, and data and AI literacy.
• Act as a strategic enabler for enterprise transformation programs and provide trusted data and decision support to the Executive Leadership Team and Board of Directors.

Data Foundations & Platforms

• Partner with the business to identify, prioritize, and sequence the master data domains where governance will deliver the highest value.
• Define the Data Governance Playbook, business glossary, critical data elements, and data ownership and stewardship workflows.
• Implement data quality standards and monitoring; embed governance into daily operations.
• Define and evolve the target data architecture: ingestion, data lake, cloud data warehouse, data catalog and lineage, semantic layer, observability, and data marketplace.
• Modernize the business intelligence estate, rationalize the reporting portfolio, and enable self-serve access to trusted KPIs.
• Support the integration of Canadian and U.S. data environments.

Advanced Analytics, AI & Trust

• Build and manage the portfolio of analytics and AI products that compound the value of the data foundation across business functions.
• Develop the talent and operating practices required to industrialize analytics and AI, including MLOps and lifecycle management.
• Operationalize the enterprise AI governance standards (responsible AI, model risk, data privacy).
• Partner with Cybersecurity and Infrastructure on data security, data classification, access governance, and the resilience of data and analytics platforms.
• Lead the data and analytics regulatory and compliance program in collaboration with Legal, Privacy, Cybersecurity, and Compliance, including Quebec Law 25, PIPEDA, US state privacy regimes, and the emerging AI regulatory landscape.
• Oversee the ethical and responsible use of data and algorithms; ensure data used for financial and legal reporting is reliable, traceable, and consistent.