Senior Scientist, Computational Materials Solutions

Location: Northern

## Senior Scientist, Computational Materials Solutions Apply locations:
Remote, PAtime type:
Full time posted on:
Posted Yesterday job requisition :
REQ-13736
*
* Job Title:

** Senior Scientist, Computational Materials Solutions
*
* Job Description:

**** The Role
** Entegris is currently seeking a
** Senior Scientist, Computational Materials Solutions
** to join our Digital Innovation Team in the Technology and Innovation organization within our Materials Solutions (MS) business. This position will be remote.

This role focuses on accelerating innovation by combining computational science, machine learning, and domain expertise to deepen scientific understanding, guide experimentation, and translate research breakthroughs into scalable technologies for Molecular & Engineering Solutions (MES) business unit. You will translate MES business and technology priorities into actionable computational approaches, including molecular and materials modeling, predictive simulation, statistical and hybrid methods, and scientific data analysis.

You will work in close partnership with experimental scientists, engineers, R&D leaders, and cross-functional technology teams to develop solutions that inform materials design, process development, product performance, and next generation technologies. The ideal candidate brings deep quantitative rigor, curiosity-driven problem solving, and the ability to bridge theory, data, and experimentation.
** What You’ll Do
*** Develop and apply molecular, statistical and computational, chemistry-informed solutions to investigate material behavior, process mechanisms, and guide materials design.
* Support MES product and technology development programs through AI/ML, predictive simulations, structure-property analysis, design-of-experiments support, optimization, and reusable computational workflows.
* Integrate simulation data, experimental results, and domain knowledge to support hypothesis generation, experiment prioritization, and model-based interpretation of R&D results.
* Perform model calibration, verification, validation, sensitivity analysis, and uncertainty quantification to build scientific confidence in computational recommendations.
* Translate computational outputs into actionable guidance for MES scientists, engineers, and leaders, including clear communication of assumptions, limitations, uncertainty, and decision implications.
* Build reusable solution libraries, workflows, documentation, and technical knowledge assets that can be applied across MES priorities and broader Materials Division R&D needs.
* Communicate modeling assumptions, results, and insights clearly to technical and non-technical stakeholders. Document methods and results in technical reports, internal publications, and knowledge repositories.
** What We Seek
*** M.S. or Ph.D. in Chemistry, Materials Science, Physics, Engineering, or related scientific discipline.
* Strong foundation in computational materials science, computational chemistry, molecular modeling, statistics, scientific computing, and simulation
* Experience supporting research, experimentation, or early-stage technology development.
* Ability to connect computational results to physical or chemical mechanisms and translate those results into practical R&D decisions.
* Working knowledge of model verification, validation, uncertainty quantification, documentation, simulation data management, model reuse, and lifecycle governance.
* Hands-on experience developing computational, simulation, or data-driven solutions using Python and scientific libraries such as Num Py, pandas, scikit-learn, PyTorch, Tensor Flow, or related tools.
* Strong collaboration, stakeholder-management, and communication skills with the ability to present complex technical work clearly to scientific, engineering, and business audiences.
** Preferred Qualifications
*** 1-3 years of experience in materials science, chemistry, semiconductor, life sciences, energy, or advanced manufacturing R&D.
* Demonstrated success combining experimental data with modeling and AI to guide discovery or development.
* Experience with design-of-experiments (DoE), optimization, or Bayesian methods.
*…