Data Scientist – Materials R&D

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

📋 Description

• Collaborate with polymer scientists, chemists, and engineers to advance bio-polymer research and development through data-driven methodologies.

• Analyze and model experimental, formulation, and process data to uncover structure-property-process relationships.

• Create predictive models aimed at enhancing material performance and property optimization, formulation design, screening, and process scale-up.

• Design and assess experiments (DOE) to optimize learning efficiency and shorten development timelines.

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

• Employ machine learning techniques such as regression, classification, clustering, and time-series modeling on intricate scientific datasets.

• Work alongside data engineering and IT teams to facilitate scalable data infrastructure for R&D initiatives.

• Share insights, trade-offs, and recommendations with both technical and non-technical stakeholders.

• Contribute to the development of data dictionaries and process flow diagrams for complex data solutions.

• Mentor junior data scientists or technical personnel and help promote data science best practices within R&D.

• Keep abreast of developments in materials informatics, polymer modeling, and applied AI in scientific research.

⛳️ Requirements

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

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

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

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

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

• A strong foundation in statistics, experimental design, and multivariate analysis.

• Proven experience applying machine learning techniques to real-world, noisy scientific or experimental datasets.

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

• Excellent communication skills with the capability to translate complex analyses into actionable insights.

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

• Preferred experience with DOE software, laboratory data management systems (LIMS), or scientific databases.

• Experience in deploying models to aid 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 performance-based bonuses.

• Comprehensive health, dental, and vision insurance.

• Retirement savings plan with company matching.

• Opportunities for professional development and continuous learning.

• Flexible work arrangements and a positive work-life balance.