Digital Agriculture Analytics Lead

''•Toronto, ON

6d•Hybrid

About The Position

McCain’s North America Agriculture organization is transforming into a digitally enabled, data- and insight-driven operation that connects on-farm activities to downstream outcomes across storage, manufacturing, sustainability, and supply chain. The Digital Agriculture Analytics Lead is a hands-on, business-focused individual contributor responsible for translating agricultural and operational challenges into actionable insights using advanced analytics and AI-enabled techniques. This role focuses on decision impact, adoption, and measurable business value, not just reports or dashboards.

Requirements

Bachelor’s degree in a relevant field, or equivalent practical experience
4–7+ years of experience delivering analytics or insights in agriculture, food, supply chain, manufacturing, or other data-rich operational environments
Proven ability to translate business or agronomic questions into analytical insights that drive decisions and measurable outcomes
Hands-on experience building and applying advanced analytics solutions such as forecasting models, predictive insights, scenario analysis, or AI-enabled decision tools
Experience using analytics and data tools such as SQL, Python (or similar), and business intelligence platforms (e.g., Power BI or equivalent)
Experience working with multiple data sources and partnering across business and technical teams
Strong business-first mindset with the ability to influence stakeholders and communicate insights clearly
Curiosity and openness to using AI-enabled analytics where it adds value, without over-engineering solutions

Nice To Haves

Willingness to travel up to 25% as required

Responsibilities

Lead analytics, AI-enabled insights, and digital innovation initiatives across North America Agriculture to deliver measurable business value across the farm-to-fork value chain
Partner with growers, field teams, manufacturing, storage, sustainability, and supply chain stakeholders to identify and prioritize high-value analytics use cases
Translate agronomic and operational challenges into actionable insights using advanced analytics, forecasting, predictive techniques, and AI-enabled approaches
Build and maintain analytical products such as models, forecasts, scenario analyses, dashboards, and decision-support tools
Define, standardize, and track key agriculture KPIs to enable consistent performance monitoring across regions and teams
Drive data standardization and harmonization in partnership with enterprise data and technology teams to enable scalable, trusted analytics
Embed analytics and digital solutions into workflows, operating rhythms, and decision forums to ensure adoption and sustained impact
Evaluate and pilot emerging analytics and digital technologies that improve performance, sustainability, and innovation
Support change management, training, and stakeholder engagement, including defining value hypotheses and success metrics