Data Scientist - Materials R&D - Remote-Travel

Itape; Marysville (MI); Hybrid; USD 150,000 - 190,000; Full time

Job summary

Itape is seeking a Senior Data Scientist to accelerate R&D in bio-polymers and sustainable materials. Based in Marysville, MI, or work remotely with travel as needed, you will apply advanced data science, modeling, and ML to experimental and process data to shorten development cycles and improve material performance.

You will build reproducible data workflows, develop predictive models for material design and process optimization, and mentor junior data scientists within the R&D team.

Qualifications

• Bachelor’s degree in a relevant field; Master’s/PhD preferred.
• Experience with data science, analytics, or scientific computing in R&D contexts.

Responsibilities

• Partner with scientists and engineers to apply data-driven methods in bio‑polymer R&D.

Skills

Python

R

SQL

Statistics

DOE

Data visualization

Cloud platforms

Education

Bachelor’s degree (Data Science / CS / Statistics / Materials Science / Chemical Engineering)

Master’s or PhD preferred

Tools

AWS

Azure

Job description

This position can be based out of Marysville, MI, or work remotely with some travel as needed.

Title: Senior Data Scientist

Department: Research and Development

Status: Exempt Salaried

Position Purpose: The Senior Data Scientist will support R&D efforts in bio-polymers and sustainable materials and 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.

Principle Accountabilities

• 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
• Understanding of data visualization best practices
• Experience working with batch or streaming data processes a plus
• 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

Essential Skills and Experience

• 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
• 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