
1001 - 5000 employees
🏭 Manufacturing
🤝 B2B
🛍️ eCommerce
Manufacturing • B2B • eCommerce
IPG is a manufacturer and supplier of adhesive tapes, packaging films, protective packaging products and automated packaging machinery. Its product portfolio includes carton-sealing tapes (acrylic, hot-melt, water-activated), industrial tapes (duct, foil, masking), packaging films (stretch, shrink, VCI), protective cushioning and mailers, and automated equipment such as case sealers, palletizers and tape heads. IPG serves business customers across e-commerce fulfillment, general manufacturing, building & construction, aerospace and other industrial sectors.
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1001 - 5000 employees
🏭 Manufacturing
🤝 B2B
🛍️ eCommerce
Manufacturing • B2B • eCommerce
IPG is a manufacturer and supplier of adhesive tapes, packaging films, protective packaging products and automated packaging machinery. Its product portfolio includes carton-sealing tapes (acrylic, hot-melt, water-activated), industrial tapes (duct, foil, masking), packaging films (stretch, shrink, VCI), protective cushioning and mailers, and automated equipment such as case sealers, palletizers and tape heads. IPG serves business customers across e-commerce fulfillment, general manufacturing, building & construction, aerospace and other industrial sectors.
• Partner with polymer scientists, chemists, and engineers to support bio-polymer research and development using data-driven methods • Analyze and model experimental, formulation, and process data to identify structure-property-process relationships • Develop predictive models for material performance and property optimization, formulation design and screening, and scale-up and process optimization • Design and analyze experiments (DOE) to maximize learning efficiency and reduce development timelines • Build and maintain reproducible data workflows for R&D data ingestion, cleaning, and analysis • Apply machine learning techniques including regression, classification, clustering, and time-series modeling to complex scientific datasets • Collaborate with data engineering and IT teams to enable scalable data infrastructure for R&D • Communicate insights, tradeoffs, and recommendations to technical and non-technical stakeholders • Contribute to data dictionaries and process flow diagrams for complex data solutions • Mentor junior data scientists or technical staff and contribute to data science best practices within R&D • Stay current with advances in materials informatics, polymer modeling, and applied AI in scientific research
• Bachelor’s degree in Data Science, Computer Science, Statistics, Materials Science, Chemical Engineering, or a related field; Master’s or PhD preferred • 10+ years of professional experience in data science, applied analytics, or scientific computing • Experience working with materials science, polymer science or chemical R&D data preferred • Strong proficiency in Python and/or R for data analysis and modeling • Solid experience with SQL and structured and semi-structured datasets • Strong foundation in statistics, experimental design, and multivariate analysis • Demonstrated experience applying machine learning to real-world, noisy scientific or experimental data • Ability to work effectively in a cross-functional R&D environment • Strong communication skills and ability to translate complex analyses into actionable insights • Familiarity with bio-polymers, sustainable materials, or polymer processing preferred • Experience with DOE software, laboratory data management systems (LIMS), or scientific databases preferred • Experience deploying models to support R&D decision-making or manufacturing scale-up preferred • Familiarity with cloud platforms such as AWS or Azure and data science lifecycle tools preferred • Prior experience mentoring or leading technical projects preferred
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