Senior Scientist, Computational Materials Solutions

Entegris

Senior Scientist advancing Entegris materials-solutions innovation through computational materials science and machine learning. Translating simulations, experimental data, and domain expertise into scalable R&D technologies.

Posted 8/11/2026full-timeRemote • Pennsylvania • 🇺🇸 United StatesSenior💰 $100,500 - $125,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in computational materials science and chemistry, utilizing machine learning and statistical methods to drive innovation in materials design and development. Proficient in translating complex computational results into actionable insights for R&D, while effectively communicating with both technical and non-technical stakeholders.

Highest-signal resume keywords

Computational Materials ScienceComputational ChemistryMachine LearningPython ProgrammingDesign-of-Experiments (DoE)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Molecular ModelingPredictive SimulationStatistical MethodsScientific Data AnalysisModel VerificationModel ValidationUncertainty QuantificationOptimizationData-Driven SolutionsSimulation Data Management

Soft Skills

CollaborationStakeholder ManagementCommunication

Tools & Technologies

NumPyPandasScikit-LearnPyTorchTensorFlow

Industry Keywords

Materials ScienceChemistrySemiconductorLife SciencesEnergyAdvanced Manufacturing

Tech Stack

Tools & technologies

NumpyPandasPythonPyTorchScikit-LearnTensorflow

About the role

Key responsibilities & impact

Accelerate innovation by combining computational science, machine learning, and domain expertise for the Materials Solutions business
Translate business and technology priorities into computational approaches including molecular and materials modeling, predictive simulation, statistical and hybrid methods, and scientific data analysis
Develop and apply computational chemistry-informed solutions to investigate material behavior, process mechanisms, and materials design
Support product and technology development 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 generate hypotheses, prioritize experiments, and interpret R&D results
Perform model calibration, verification, validation, sensitivity analysis, and uncertainty quantification
Translate computational outputs into actionable guidance for scientists, engineers, and leaders
Build reusable solution libraries, workflows, documentation, and technical knowledge assets
Communicate modeling assumptions, results, and insights to technical and non-technical stakeholders
Document methods and results in technical reports, internal publications, and knowledge repositories

Requirements

What you’ll need

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 them 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 NumPy, pandas, scikit-learn, PyTorch, TensorFlow, or related tools
Strong collaboration, stakeholder-management, and communication skills
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
Familiarity with visualization, notebooks, or technical storytelling for R&D audiences
Publications, patents, or significant internal research contributions
Entegris does not provide immigration-related sponsorship; applicants must not require Entegris immigration sponsorship now or in the future

Benefits

Comp & perks

Generous 401(K) plan with an impressive employer match
Excellent health, dental and vision insurance packages to fit your needs
Flexible work schedule
11 paid holidays a year
Paid time off (PTO) policy that empowers you to take the time you need to recharge
Education assistance to support your learning journey
Values-driven culture with colleagues that rally around People, Accountability, Creativity and Excellence