VP, Data Analytics & Business Intelligence

Graphic Packaging Careers•Sandy Springs, GA

18h

About The Position

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.

Requirements

Requires bachelor’s degree in information management, data science/analytics, industrial engineering, computer science, or similar fields; advanced degree preferred.
15+ years of overall experience in data, analytics, and information management, including senior leadership roles.
Minimum of 5 years leading global data and analytics teams in an industrial manufacturing environment (multi-site/plant experience strongly preferred).
Hands-on knowledge of manufacturing systems and data sources (e.g., ERP, MES, historian/time-series, SCADA, IIoT platforms, quality systems, EAM/CMMS).
Knowledge of industry-leading ETL/ELT tools and practices, data modeling, data quality, and metadata/cataloging required.
Experience implementing enterprise data warehousing/lakehouse patterns and data products, preferably in cloud or hybrid architectures.
Strong knowledge of data visualization and industry-leading tools; experience standardizing KPIs such as OEE, yield, scrap, downtime, and service level.
Demonstrated competency delivering business outcomes using predictive analytics, optimization, and AI/ML (including time-series forecasting/anomaly detection) with measurable ROI.
Experience establishing MLOps practices and operating models (deployment, monitoring, drift detection, retraining, documentation).
Proven experience leading complex, cross-functional programs (multi-site manufacturing + IT/OT) using formal program management practices; PMP/Lean/Six Sigma certification a plus.
Experience designing and operating middleware/integration capabilities (API management, iPaaS/ESB, event streaming/queues, EDI) and integrating OT systems (MES/SCADA/historians) with enterprise and cloud data platforms.
Experience with GenAI concepts and governance (evaluation, guardrails, security/privacy, IP considerations) and ability to identify high-value enterprise use cases.
Understanding of controls and compliance in regulated environments, including change control, SOX, and ITGC; partnership with cybersecurity for OT/IT and AI risk management.

Nice To Haves

Advanced degree preferred.
PMP/Lean/Six Sigma certification a plus.

Responsibilities

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 solutions.
Evaluate and deploy GenAI capabilities where valuable (e.g., maintenance copilots, knowledge search across SOPs/work instructions, automated insights, narrative reporting), with security and compliance controls.
Lead and develop a high-performing global team (data engineering, analytics engineering, data science, BI, and platform) including goal setting, coaching, and performance management.
Compile and manage annual LRP and OPEX budgets for the function; track value realization and ROI for analytics/AI initiatives.
Regularly prepare management reports and participate in Steering Committee meetings as required.
Participate in M&A work to integrate acquired businesses into the company technology and data stack, including harmonization of manufacturing data sources and KPIs.
Work with internal audit, external audit, and management for change control, SOX, ITGC, and other compliance requirements.
Other duties as assigned.