Data Scientist - Materials R&D - Remote-Travel

Intertape Polymer Group (IPG)•Marysville, MI

3d•Remote

About The Position

The Senior Data Scientist will support R&D efforts in bio-polymers and sustainable materials, focusing on applying advanced data science, statistical modeling, and machine learning to experimental, process, and materials data to accelerate innovation, improve material performance, and reduce development cycles. This position can be based out of Marysville, MI, or work remotely with some travel as needed.

Requirements

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 working with 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 with the ability to translate complex analyses into actionable insights

Nice To Haves

Understanding of data visualization best practices
Experience working with batch or streaming data processes a plus
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 (e.g., AWS, Azure) and data science lifecycle tools, preferred
Prior experience mentoring or leading technical projects, preferred

Responsibilities

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 to support: Material performance and property optimization, Formulation design and screening, 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 (e.g., regression, classification, clustering, 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 clearly 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