Find Jobs
Find Jobs Near You – Available Work in Your Location
Skip to job details
N
Novolex
Lead Data Scientist, Manufacturing & AI
Career Insights for Data Scientist
See where this job fits in the broader career landscape. Knowing your career path helps you see what's possible from here.
Scorecard
Based on Illinois data
Review key factors to help you decide if this role fits your goals. How is this calculated?
What they do
A Data Scientist utilizes skills and experience to systematically answer questions using data to provide actionable recommendations. Commonly utilizes advanced statistical analysis and machine learning techniques. Common responsibilities also include data cleaning and data management.
$101,074 / year median in Illinois
+16% projected growth
Job Description
Company Overview Why Choose Us? Novolex is a leading manufacturer of food, beverage, and specialty packaging that supports multiple industries including foodservice, restaurant delivery and carryout, food processing, grocery and retail, and industrial sectors. Novolex manufacturing and sourcing expertise spans a diverse range of substrates including resin, paper, molded fiber, aluminum and more. We provide customers a broad array of stock and customized solutions with 120 product categories, 250 brands and over 39,000 SKUs. Our Sustainability Commitment The Novolex sustainability vision is built upon three pillars: our products, our operations and our people. Each is critically important to our growth and future as a business. These pillars form the foundation of our company-wide commitment to sustainability, helping us achieve our ambitious goals through our wide-ranging initiatives. Job Description Job Overview The Lead Data Scientist provides technical and people leadership for enterprise Manufacturing AI, Machine Learning, and Industry 4.0 initiatives supporting the Digital Manufacturing organization. This role is responsible for developing and deploying scalable AI-driven solutions that improve safety, quality, reliability, productivity, asset performance, and Overall Equipment Effectiveness (OEE) across a multi-plant manufacturing network. The ideal candidate would be located in the Chicago, Charlotte, Atlanta, or Dallas area.
The position partners with Operational Excellence, Asset Care, Engineering, IT, Supply Chain, Plant Leadership, and Digital Manufacturing teams to build enterprise analytics capabilities and accelerate the organization's digital transformation strategy. Key Responsibilities Manufacturing AI & Industry 4.0 Lead the enterprise Manufacturing AI roadmap supporting predictive, prescriptive, and autonomous manufacturing. Develop AI/ML solutions for predictive maintenance, process optimization, quality prediction, energy optimization, and throughput improvement. Drive adoption of Industry 4.0 technologies including connected assets, IIoT, digital manufacturing platforms, and advanced analytics. Identify high-value AI use cases and deliver measurable business value across multiple manufacturing sites. Digital Manufacturing Platforms Develop solutions leveraging Microsoft Fabric, Azure AI, Azure Machine Learning, Azure Databricks, PI System, Insight, Vorne, MES platforms, ERP systems, and Industrial IoT data. Integrate historian, machine, quality, maintenance, and operational data into scalable analytics solutions. Establish enterprise standards for data governance, model lifecycle management, and reusable AI assets. People Leadership Lead, coach, mentor, and develop Data Scientists and Analytics professionals. Provide technical direction, code reviews, model reviews, and career development. Build a high-performing analytics team and foster a culture of innovation and continuous improvement. Manage project prioritization, workload planning, and resource allocation. Business Leadership Partner with leadership to define the Digital Manufacturing analytics roadmap. Translate business strategy into AI and analytics initiatives aligned with Operational Excellence objectives. Present recommendations and business cases to senior executives. Qualifications Bachelor's degree in Computer Science, Data Science, Engineering, Statistics, Industrial Engineering, or related field required. Master's degree preferred 8+ years of Data Science, AI, or Machine Learning experience. 3-5 years leading technical teams or Data Scientists. Manufacturing, industrial automation, or process manufacturing experience required. Technical Expertise Python, SQL, Spark/PySpark, R, Git Machine Learning, Deep Learning, Time Series Forecasting, Computer Vision, Optimization, MLOps Microsoft Fabric, Azure AI Foundry/Azure AI Services, Azure Machine Learning, Azure Databricks OSIsoft PI System, Insight, Vorne, MES, ERP, Industrial IoT platforms Power BI, Tableau, enterprise dashboard development Leadership Competencies Strategic Leadership Enterprise Digital Transformation Manufacturing AI Industry 4.0 Operational Excellence Cross-functional Influence Executive Communication Coaching & Talent Development Innovation Business Acumen Success Measures Accelerate enterprise AI adoption across manufacturing. Improve OEE, reliability, quality, and cost through deployed AI solutions. Establish standardized Manufacturing AI architecture and governance. Develop a high-performing Data Science team. Deliver measurable financial impact supporting the Digital Manufacturing strategy.
The position partners with Operational Excellence, Asset Care, Engineering, IT, Supply Chain, Plant Leadership, and Digital Manufacturing teams to build enterprise analytics capabilities and accelerate the organization's digital transformation strategy. Key Responsibilities Manufacturing AI & Industry 4.0 Lead the enterprise Manufacturing AI roadmap supporting predictive, prescriptive, and autonomous manufacturing. Develop AI/ML solutions for predictive maintenance, process optimization, quality prediction, energy optimization, and throughput improvement. Drive adoption of Industry 4.0 technologies including connected assets, IIoT, digital manufacturing platforms, and advanced analytics. Identify high-value AI use cases and deliver measurable business value across multiple manufacturing sites. Digital Manufacturing Platforms Develop solutions leveraging Microsoft Fabric, Azure AI, Azure Machine Learning, Azure Databricks, PI System, Insight, Vorne, MES platforms, ERP systems, and Industrial IoT data. Integrate historian, machine, quality, maintenance, and operational data into scalable analytics solutions. Establish enterprise standards for data governance, model lifecycle management, and reusable AI assets. People Leadership Lead, coach, mentor, and develop Data Scientists and Analytics professionals. Provide technical direction, code reviews, model reviews, and career development. Build a high-performing analytics team and foster a culture of innovation and continuous improvement. Manage project prioritization, workload planning, and resource allocation. Business Leadership Partner with leadership to define the Digital Manufacturing analytics roadmap. Translate business strategy into AI and analytics initiatives aligned with Operational Excellence objectives. Present recommendations and business cases to senior executives. Qualifications Bachelor's degree in Computer Science, Data Science, Engineering, Statistics, Industrial Engineering, or related field required. Master's degree preferred 8+ years of Data Science, AI, or Machine Learning experience. 3-5 years leading technical teams or Data Scientists. Manufacturing, industrial automation, or process manufacturing experience required. Technical Expertise Python, SQL, Spark/PySpark, R, Git Machine Learning, Deep Learning, Time Series Forecasting, Computer Vision, Optimization, MLOps Microsoft Fabric, Azure AI Foundry/Azure AI Services, Azure Machine Learning, Azure Databricks OSIsoft PI System, Insight, Vorne, MES, ERP, Industrial IoT platforms Power BI, Tableau, enterprise dashboard development Leadership Competencies Strategic Leadership Enterprise Digital Transformation Manufacturing AI Industry 4.0 Operational Excellence Cross-functional Influence Executive Communication Coaching & Talent Development Innovation Business Acumen Success Measures Accelerate enterprise AI adoption across manufacturing. Improve OEE, reliability, quality, and cost through deployed AI solutions. Establish standardized Manufacturing AI architecture and governance. Develop a high-performing Data Science team. Deliver measurable financial impact supporting the Digital Manufacturing strategy.