Requisition ID: 35823


Job Description:

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Overview:
McCain is undergoing a global transformation, leveraging advanced data analytics, AI, and digital tools to create a connected manufacturing ecosystem. This transformation aims to drive operational excellence across our 50+ global manufacturing sites, with a focus on energy reduction, asset reliability, and process improvements to support sustainable and efficient production.
 

This high-impact, high-visibility role requires expertise in analytics, industrial IoT, AI/ML, and automation/controls, with a focus on using data to drive decision-making and capital investments. The ideal candidate will act as a strategic thought leader and trusted advisor to senior leadership, shaping McCain s manufacturing strategy.

Role:
This role drives the strategy, design, and implementation of manufacturing analytics across McCain s global sites, leveraging data-driven insights to enhance energy efficiency, asset reliability, and optimizing core manufacturing processes. The candidate will be a technical expert in industrial systems (e.g., PI, MES, SCADA, PLCs, AI/ML, cloud platforms), applying scientific principles and analytical solutions to improve efficiency, innovate processes, and support sustainability goals. They will collaborate with cross-functional teams, senior leadership, and global plant operations, using strong communication and strategic influence to align efforts with operational and scientific objectives.

Key Responsibilities:

Strategic Leadership & Innovation:

• Develop and guide a global manufacturing roadmap that integrates scientific insights, predictive tools, and automation to achieve energy reduction, asset reliability, and process improvements.
• Serve as a technical expert in manufacturing intelligence, leading initiatives rooted in science and analytics to enhance efficiency, reduce costs, and prioritize sustainability.
• Advocate for predictive analytics and real-time decision-making tools, particularly for energy-intensive processes, drawing on scientific methodologies.
• Communicate business cases and ROI to senior leaders for initiatives targeting energy savings, reliability, and process optimization, grounded in data and scientific analysis.

Technical Expertise & Execution:

• Design manufacturing intelligence platforms by integrating industrial systems (PI, MES, SCADA, PLCs) with analytical tools and cloud-based solutions to address energy reduction (e.g., optimizing freezers), asset reliability (e.g., predictive maintenance), and other process improvements.
• Develop scalable frameworks to analyze OEE, quality, energy usage, and manufacturing unit operations leveraging principles from chemistry, physics, and mathematics.
• Apply advanced modeling techniques, including AI/ML where appropriate, for predictive analysis, anomaly detection, and root cause analysis to solve energy inefficiencies, equipment issues, and other process challenges.
• Support global implementation efforts, ensuring solutions are practical and adopted effectively across teams.

Global Manufacturing Optimization & Value Creation:

• Use scientific and analytical approaches to optimize production efficiency, minimize downtime, improve quality, reduce energy use, and enhance manufacturing processes across global plants.
• Implement monitoring and analytics solutions to support decisions like optimizing energy in freezing systems, predicting equipment performance, and refining freezing cycle efficiency, rooted in scientific understanding.
• Partner with supply chain, manufacturing, and technical teams to align analytics with operational goals, emphasizing energy and reliability.
• Pursue continuous improvement through the application of advanced technologies and scientific principles to meet energy, reliability, and process objectives.

Capability Development & Change Leadership:

• Lead training initiatives to build analytical and scientific fluency among manufacturing teams, focusing on energy, reliability, and other process enhancements.
• Support adoption of analytical tools and processes through effective communication and change leadership.

Qualifications & Experience:

Required:

• Bachelor s or Master s degree in Manufacturing, Industrial Engineering, Data Science, Computer Science, AI/ML, or a related field.
• 15+ years of experience in manufacturing analytics, digital transformation, and industrial automation, including 5+ years in a senior leadership role.
• Expertise in manufacturing data platforms (OSIsoft PI, MES, SCADA, PLCs, IoT, cloud analytics, AI/ML).
• Expertise in applying scientific principles (chemistry, physics, math) and advanced analytics to solve manufacturing challenges, particularly in energy reduction and freezing systems.
• Experience leading global projects and collaborating across functions, with a focus on energy efficiency, reliability, and process optimization.
• Proficiency in analytical tools and programming (e.g., Python, SQL, R, Power BI, Tableau) and a strong grasp of enterprise data frameworks.
• Demonstrated ability to quantify business value and develop ROI-driven strategies using scientific and analytical methods.
• Deep understanding of OEE, quality control, energy management, and asset reliability in a global manufacturing context.
• Strong skills in organizational change, team collaboration, and stakeholder engagement.

Preferred:

• Ph.D. in Chemistry, Physics, Mathematics, or a related field
• Certified Energy Manager (CEM) certification.
• Certified Lean Six Sigma Black Belt, PMP, or expertise in Continuous Improvement/Lean methodologies, with applications in energy and reliability.
• Experience with cloud platforms (AWS, Azure, GCP) and analytical frameworks (TensorFlow, PyTorch) as tools to support scientific analysis.
• Knowledge of supply chain dynamics and their connection to manufacturing, especially in energy and freezing process optimization.
• Background in food manufacturing or CPG, with hands-on expertise in industrial freezing systems (e.g., blast freezers, spiral freezers).
• Familiarity with energy management systems and reliability-centered maintenance (RCM) principles.

Compensation Package: $111,700.00 -$149,000.00  USD annually + bonus eligibility

The above reflects the target compensation range for the position at the time of posting. Hiring compensation will be determined based on experience, skill set, education/training, and other organizational needs.