Data Scientist – Materials R&D
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Job Description
Senior Data Scientist applying machine learning and statistical modeling to bio-polymer and sustainable materials R&D. Accelerating material innovation, performance optimization, and scale-up decisions.
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 for 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 including regression, classification, clustering, and 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 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
Requirements:
- 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 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 and 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 such as AWS or Azure and data science lifecycle tools preferred
- Prior experience mentoring or leading technical projects preferred
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