VP, Data Analytics & Business Intelligence

The VP, Data Analytics & Business Intelligence for Graphic Packaging will set the strategic vision for modernizing our data analytics and Artificial Intelligence (AI) capabilities and execute the plan to achieve business goals, leveraging modern platforms, data products, and advanced analytics to improve safety, quality, throughput, reliability, and cost across industrial manufacturing operations, while also partnering to enable key digital operations platforms (public website, SharePoint, and low-code solutions) that support enterprise communication and productivity.

The successful candidate will inventory the current set of tools, technologies, and capabilities; partner with IT, OT, plant leadership, business leaders, and enterprise architecture to create a strategic vision to move from predominantly rear-view reporting to predictive and prescriptive analytics and AI-driven decision support
. This leader will simplify the current BI & reporting footprint; build a modern, cloud-enabled data ecosystem that integrates ERP, MES, historian/time-series data, SCADA/IIoT, quality systems, maintenance/EAM, and supply chain data
; and enable global self-service analytics and governed citizen data science where appropriate.

• Baseline current set of tools, technologies, and capabilities across BI/reporting, advanced analytics, and AI/ML (including data pipelines, semantic layers, and self-service tooling).
• Define and drive an industrial data strategy that connects enterprise (ERP) and plant-floor (OT) data, including MES, historians/time-series, SCADA/IIoT, quality systems, and maintenance/EAM.
• Identify gaps in business analytics and industrial AI and work to fill them through new tools/technologies, vendor partnerships, and talent development.
• Build and scale use cases that improve manufacturing performance, including predictive maintenance, yield optimization, scrap reduction, OEE improvement, downtime/root-cause analytics, process capability (SPC) insights, and energy/utilities optimization.
• Establish a modern data platform (cloud and/or hybrid) that can harness financial, operational, and machine data—including high-frequency time-series—and enable secure edge-to-cloud patterns where needed.
• Own the middleware and integration approach that enables reliable data and event movement across ERP, MES, historians, quality, maintenance, and cloud platforms—leveraging APIs, iPaaS/ESB patterns, event streaming, and edge gateways—with standards for security, monitoring, and scalability.
• Implement MLOps and model lifecycle management (deployment, monitoring, drift, retraining) and partner with cybersecurity to ensure OT/IT and AI controls are designed appropriately.
• Develop and enforce data governance and AI governance (data quality, lineage, master/reference data, access controls, model risk, responsible AI) aligned to SOX/ITGC and internal audit requirements.
• Partner with the technology transformation leader to deliver critical reporting and analytics solutions identified in the transformation roadmap and simplify the data, reporting, and analytics landscape.
• Create a scalable data product operating model and prioritize a roadmap that balances quick wins with foundational capabilities and plant-by-plant adoption.
• Own end-to-end program management for the entire IT portfolio, including governance cadence, integrated roadmap, milestones, dependency management, RAID (risks, assumptions, issues, decisions), and benefits/value tracking.
• Enable governed self-service analytics and citizen data science capabilities, including training, standards, reusable assets, and guardrails.
• Provide executive oversight for digital operations platforms, including the public website and SharePoint, ensuring a secure, reliable, and scalable operating model (content lifecycle, release/change management, availability, performance, and vendor/partner management).
• Establish governance and enablement for the Microsoft Power Platform (Power Apps, Power Automate, and related tools), including environment strategy, security/DLP policies, ALM standards, support/ops processes, and citizen-developer training to safely scale low-code…