Data Scientist – Materials R&D

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

📋 Description

• Assist R&D initiatives in bio-polymers and sustainable materials utilizing advanced data science, statistical modeling, and machine learning.

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

• Analyze and model experimental, formulation, and process data to discern structure–property–process relationships.

• Create predictive models for material performance, formulation design, scale-up, and process optimization.

• Design and evaluate experiments (DOE) to enhance learning efficiency and shorten development timelines.

• Establish and uphold reproducible data workflows for R&D data ingestion, cleansing, and analysis.

• Implement machine learning techniques including regression, classification, clustering, and time-series modeling on scientific datasets.

• Work in conjunction with data engineering and IT teams to facilitate a scalable R&D data infrastructure.

• Convey insights, trade-offs, and recommendations to both technical and non-technical stakeholders.

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

• Mentor junior data scientists or technical personnel and aid in establishing R&D data science best practices.

• Stay updated with advancements 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 is required; a Master's or PhD is preferred.

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

• Experience with materials science, polymer science, or chemical R&D data is preferred.

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

• Extensive experience with SQL as well as structured and semi-structured datasets.

• Robust foundation in statistics, experimental design, and multivariate analysis.

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

• Capability to work efficiently in a cross-functional R&D environment.

• Excellent communication skills and the ability to translate 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.

• Background 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 mentoring or leading technical projects is preferred.

🏝️ Benefits

• Competitive salary and performance-based bonuses.

• Comprehensive health, dental, and vision insurance.

• Opportunities for professional development and continuous learning.

• Flexible work hours and remote work options.

• Collaborative and innovative work environment.