Head of Data, ML & Analytics

Enterprise Data Strategy & Architecture

Define and own the enterprise data strategy, reference architecture, and roadmap, aligning with the company’s technology strategy and sustainability goals.

Stand up and evolve the enterprise data platform (Microsoft Fabric or Azure Synapse), integrating core systems and external data sources while enforcing scalable, secure patterns.

Drive data modeling, master/metadata management, data quality, lineage, and observability across the data lifecycle.

Analytics & Business Intelligence

Institutionalize enterprise reporting and self-service analytics; standardize executive and functional dashboards in Power BI as the enterprise reporting platform.

Partner with business teams to translate questions into analytic products (KPIs, forecasts, optimization models) and deliver them reliably and repeatably.

Applied Machine Learning & Advanced Analytics

Build a pragmatic ML portfolio aligned with AI initiatives and focused on measurable outcomes (yield optimization, demand forecasting, inventory/supply planning, pricing, sustainability metrics).

Establish model governance and MLOps practices; ensure responsible AI/ML aligned to regulatory, privacy, and cybersecurity standards.

Data Governance, Risk & Compliance

Chair/enable the enterprise data governance function (policies, standards, stewardship) to ensure integrity, security, and approved publication of reports/data assets across the organization, consistent with enterprise policy.

Collaborate with Security, Audit, and Compliance to meet legal, ESG, and market disclosure obligations.

Platform Engineering & Integration

Oversee data platform engineering, pipelines, and integrations; ensure performance, reliability, and cost efficiency across cloud resources.

Set and enforce development standards, CI/CD, and test automation for data products.

Ledership, Partnership & Change Management

Create strong partnerships with Technology (including the CITO’s office), Operations, Finance, and Commercial leaders to prioritize investments and deliver outcomes.

Lead change management and data literacy programs to scale adoption of analytics and AI.

Financial & Vendor Management

Own the data/analytics budget; optimize cloud and licensing costs (e.g., Microsoft Fabric/Power Platform, Power BI) and manage vendor relationships.