Advanced R&D Engineer​/Scientist

Job Description

At Solstice Advanced Materials, we are committed to offering the highest value-add specialty solutions in the advanced materials sector. Our goal is to solve our customers' most complex challenges through a robust and innovative product portfolio and by doing so, deliver exceptional value to our stakeholders. We have identified actionable strategies to grow by expanding into new products and markets and through strategic acquisitions, while keeping our top operating margins.

Joining our team means becoming part of an organization which leverages its long-standing reputation to capture growth trends by investing in innovation and manufacturing enhancements and maintaining deep customer relationships. We foster a collaborative and inclusive work environment that values contributions and supports professional development. With a focus on innovation and sustainability, the team is dedicated to delivering value and making a meaningful impact in advancing our customers' success.

Let’s make that impact together.

The Data Scientist for Solstice will play a critical role in our research and development efforts by applying statistical methods, data analysis techniques, and machine learning algorithms to derive insights from complex datasets. The ideal candidate will excel in designing and implementing data-driven approaches to enhance our understanding of advanced materials, contributing to innovative products and technologies. This position is available in Buffalo, NY, or Morris Plains, NJ.

Become part of an innovative team making a difference!

• Analyze large datasets using statistical and machine learning techniques to identify trends, patterns, and relationships in the discovery and manufacturing of advanced materials.
• Develop predictive models to enhance material performance characteristics and to evaluate the effects of different variables on material properties.
• Collaborate with chemists, material scientists, and engineers to understand their data needs and provide solutions that drive product development.
• Work with stakeholders across the organization to communicate analytical findings and influence decision‑making processes.
• Investigate and implement state‑of‑the‑art data science techniques to solve complex problems related to advanced materials.
• Conduct literature reviews to stay updated on the latest advancements in materials science and data analytics.
• Design, build, and maintain data pipelines and databases to collect, store, and process experimental and analytical data efficiently.
• Ensure data integrity and perform regular data quality checks to maintain reliable datasets.
• Create robust visualizations that effectively communicate insights to both technical and non‑technical audiences.
• Prepare detailed reports and presentations summarizing analytical results and recommendations.

• Master’s or Ph.D. in Data Science, Materials Science, Statistics, Applied Mathematics, or a related field.
• Minimum of 3‑5 years of experience in data analysis or data science roles, within the material science or engineering domain.
• Proficiency in programming languages such as Python and familiarity with data manipulation libraries.
• Strong understanding of statistical modeling, machine learning algorithms, and data visualization techniques.
• Experience with data visualization tools or packages (Tableau, Power BI, Origin, matplotlib, seaborn) and databases (SQL, No
  
  SQL).

• Deep knowledge of advanced materials, including polymer science, nanomaterials, ceramics, metals, small molecules and formulations, or composites.
• Familiarity with experimental methods in materials characterization and testing.
• Familiarity with chemical manufacturing processes, process modeling, and process optimization.
• Excellent problem‑solving abilities and critical thinking skills.
• Strong interpersonal and communication skills, capable of engaging with technical and non‑technical audiences.
• Ability to work independently and manage multiple projects simultaneously in a fast‑paced environment.
• Continuous learning within the field of data science and materials science.

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