Data Scientist – Materials R&D

This is a fully remote position, open to applicants in Michigan.

📋 Description

• Assist in R&D initiatives focused on bio-polymers and sustainable materials through the application of advanced data science, statistical modeling, and machine learning.

• Collaborate with polymer scientists, chemists, and engineers in the research and development of bio-polymers.

• Analyze and model data from experiments, formulations, and processes to uncover structure–property–process relationships.

• Create predictive models that assess material performance, design formulations, scale-up processes, and optimize workflows.

• Design and evaluate experiments to enhance learning efficiency and minimize development timelines.

• Establish and maintain reproducible workflows for the ingestion, cleaning, and analysis of R&D data.

• Utilize machine learning methodologies including regression, classification, clustering, and time-series analysis.

• Work together with data engineering and IT teams to develop scalable R&D data infrastructures.

• Present insights, trade-offs, and recommendations to both technical and non-technical audiences.

• Contribute to the creation of data dictionaries and process flow diagrams for intricate data solutions.

• Mentor junior data scientists or technical staff and promote best practices in R&D data science.

• Stay updated on the latest advancements in materials informatics, polymer modeling, and the application of AI in scientific research.

⛳️ Requirements

• A Bachelor's degree in Data Science, Computer Science, Statistics, Materials Science, Chemical Engineering, or a related discipline is required; a Master's or PhD is preferred.

• Over 10 years of professional experience in data science, applied analytics, or scientific computing.

• Preferred experience in materials science, polymer science, or chemical R&D data.

• Strong expertise in Python and/or R for data analysis and modeling purposes.

• Extensive experience with SQL and both structured and semi-structured datasets.

• Solid grounding in statistics, experimental design, and multivariate analysis.

• Proven experience in applying machine learning techniques to real-world, complex scientific or experimental data.

• Ability to function effectively within a cross-functional R&D environment.

• Excellent communication skills with the capacity to convert complex analyses into actionable insights.

• Familiarity with bio-polymers, sustainable materials, or polymer processing is preferred.

• Experience with DOE software, laboratory data management systems (LIMS), or scientific databases is preferred.

• Experience in deploying models for R&D decision-making or manufacturing scale-up is preferred.

• Familiarity with cloud platforms like AWS or Azure and data science lifecycle tools is preferred.

• Previous experience in mentoring or leading technical projects is preferred.

🏝️ Benefits

• Competitive salary and comprehensive benefits package.

• Opportunities for professional development and career advancement.

• Collaborative and innovative work environment.

• Access to cutting-edge technology and resources.

• Flexible work arrangements to promote work-life balance.