Job Overview
Support R&D efforts in bio-polymers and sustainable materials by applying advanced data science, statistical modeling, and machine learning to experimental, process, and materials data. The role aims to accelerate innovation, improve material performance, and reduce development cycles through data-driven methods.
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, and 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.
Qualifications
- 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.
