Sr Advanced R&D Engr/Scientist

Solstice Advanced Materials•Spokane, WA

1d•$127,865 - $159,500•Hybrid

About The Position

As a Sr Advanced R&D Engineer/Scientist here at Solstice, you will drive innovation, develop cutting-edge technologies, and lead advanced research initiatives to enhance aerospace safety, efficiency, and reliability. You will report directly to our Sr R&D Manager, and you’ll work out of our Spokane, WA location on a hybrid schedule. 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. As a Sr Advanced R& D Engineer/Scientist you will develop and apply statistical, machine learning, and scientific modeling methods to accelerate materials discovery, product development, and manufacturing optimization. The role will partner closely with scientists and engineers to transform experimental and process data into validated models, actionable insights, and scalable R&D capabilities. This position is available in Morris Plains, NJ, or Buffalo, NY.

Requirements

Master’s or Ph.D. in Data Science, Materials Science, Statistics, Applied Mathematics, or a related field.
Minimum of 5 years of experience in data analysis or data science roles within a materials science or engineering domain.
Proficiency in programming languages such as Python and familiarity with data manipulation libraries relevant to chemistry and materials (e.g., RDKit).
Experience developing physics-informed neural networks, surrogate models, cheminformatics models, or quantitative structure–property relationship models.
Strong understanding of statistical modeling, machine learning algorithms, and data visualization techniques.
Experience with data visualization tools or packages (e.g., Tableau, Power BI, Origin, matplotlib, seaborn) and databases (e.g., SQL, NoSQL).

Nice To Haves

Deep knowledge of advanced materials, including polymers, nanomaterials, ceramics, metals, small molecules, 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.
Commitment to continuous learning in data science and materials science.

Responsibilities

Analyze complex experimental, analytical, and manufacturing datasets using statistical and machine learning methods to identify trends, patterns, and relationships.
Develop and validate predictive models to improve material performance, evaluate the effects of key variables, and support product and process development.
Partner with chemists, materials scientists, engineers, and business stakeholders to define data needs, develop analytical solutions, and influence decisions.
Evaluate and implement state-of-the-art data science methods for advanced materials applications, informed by relevant scientific and technical literature.
Design, build, and maintain scalable data pipelines and databases for experimental and analytical data.
Establish data quality practices that ensure reliable, traceable, and reproducible datasets and analyses.
Communicate findings, recommendations, and technical limitations through clear visualizations, reports, and presentations for technical and non-technical audiences.