IPG is seeking a Senior Data Scientist to support R&D efforts in bio-polymers and sustainable materials. The role focuses on applying advanced data science, statistical modeling, and machine learning to experimental, process, and materials data to accelerate innovation and improve material performance. Key responsibilities include partnering with polymer scientists, chemists, and engineers to apply data-driven methods to bio-polymer research; analyzing structure–property–process relationships from experimental, formulation, and process data; developing predictive models for material performance and formulation design; designing and analyzing experiments (DOE) to maximize learning and reduce development timelines; building and maintaining reproducible data workflows for data ingestion, cleaning, and analysis; applying machine learning techniques (regression, classification, clustering, time-series) to complex scientific datasets; enabling scalable data infrastructure for R&D; communicating insights and tradeoffs to technical and non-technical stakeholders; mentoring junior data scientists and contributing to best practices; staying current with advances in materials informatics and polymer modeling. Qualifications include a Bachelor’s degree in a related field (Master’s or PhD preferred), 10+ years of experience in data science or scientific computing, strong Python and/or R skills, SQL, statistics and experimental design, and experience applying ML to real-world data. Experience with bio-polymers or polymer processing, DOE software, LIMS, or scientific databases, and cloud

IPG is seeking a Senior Data Scientist to support R&D efforts in bio-polymers and sustainable materials. The role focuses on applying advanced data science, statistical modeling, and machine learning to experimental, process, and materials data to accelerate innovation and improve material performance. Key responsibilities include partnering with polymer scientists, chemists, and engineers to apply data-driven methods to bio-polymer research; analyzing structure–property–process relationships from experimental, formulation, and process data; developing predictive models for material performance and formulation design; designing and analyzing experiments (DOE) to maximize learning and reduce development timelines; building and maintaining reproducible data workflows for data ingestion, cleaning, and analysis; applying machine learning techniques (regression, classification, clustering, time-series) to complex scientific datasets; enabling scalable data infrastructure for R&D; communicating insights and tradeoffs to technical and non-technical stakeholders; mentoring junior data scientists and contributing to best practices; staying current with advances in materials informatics and polymer modeling. Qualifications include a Bachelor’s degree in a related field (Master’s or PhD preferred), 10+ years of experience in data science or scientific computing, strong Python and/or R skills, SQL, statistics and experimental design, and experience applying ML to real-world data. Experience with bio-polymers or polymer processing, DOE software, LIMS, or scientific databases, and cloud