Job Description Summary

The Material Behaviors & Informatics Engineer will develop and deploy computational statistical and probabilistic frameworks towards the prediction optimization and characterization of next-generation alloys and chemistries for gas turbine applications.

Job Description

Responsibilities:

• Design and deploy robust computational statistical and probabilistic models to predict material properties and behavior from existing datasets field performance data and scientific literature
• Use data-driven techniques to identify new next-generation alloys and optimize known materials and material testing programs
• Create and manage material data bases
• Define the technical roadmap for materials informatics initiatives establishing best practices and methodologies
• Partner with materials engineers and design engineers to translate computational insights into practical applications
• Provide mentorship to team members fostering a culture of innovation and continuous learning
• Present findings and recommendations to technical and business stakeholders translating complex analyses into strategic decisions

Required Qualifications:

• Bachelors Masters or Ph.D. degree in Materials Metallurgical or Mechanical Engineering Data or Computer Science or related discipline from an accredited college or university.
• At least 4 years relevant experience in materials.
• Strong foundation in materials science fundamentals.
• Ability to access and handle US Export Controlled information.
• Ability to work hybrid/onsite out of Greenville SC office.

Desired Characteristics:

• Strong foundation in structure-property-behavior relationships of alloys like nickel steel and materials like ceramics and their strengthening mechanisms.
• Familiarity with modeling of mechanical and physical properties of these materials.
• Knowledge of statistical characterization methods such as Gaussian and Bayesian distributions.
• Knowledge of computational materials science and tools such as Calphad Crystal Plasticity Density Functional Theory.
• Familiarity with material testing characterization and interpretation of results.
• Experience in developing statistical/probabilistic models for regression optimization and prediction related tasks.
• Familiarity of translating research into practical engineering applications.
• Familiarity with relevant python libraries for machine learning optimization regression and visualization (scikit-learn pytorch scipy seaborn matplotlib etc.)
• Familiarity with different categorical and regression-based machine learning algorithms and knowledge of their strengths and limitations
• Familiarity with supervised semi-supervised and un-supervised machine learning algorithms
• Familiarity with multi-objective optimization
• Knowledge of computer vision fundamentals (object detection segmentation classification tracking) and models
• Strong organizational skills and demonstrated ability to drive projects to completion.
• Effective at working independently on complex tasks and managing multiple priorities under tight deadlines.
• Strong verbal and written communication skills.
• Ability and willingness to work effectively as part of a high performance cross-functional team to drive impactful results.
• Strong people skills to collaborate with team members and support tasks.

This role requires access to U.S. export-controlled information. If applicable final offers will be contingent on ability to obtain authorization for access to U.S. export-controlled information from the U.S. Government.

Additional Information

For candidates applying to a U.S. based position the pay range for this position is between $89300.00 and $148700.00. The Company pays a geographic differential of 110% 120% or 130% of salary in certain areas. The specific pay offered may be influenced by a variety of factors including the candidates experience education and skill set.

Bonus eligibility: discretionary annual bonus.

This posting is expected to remain open for at least seven days after it was posted on January 05 2026.

Available benefits include medical dental vision and prescription drug coverage; access to Health Coach from GE Vernova a 24/7 nurse-based resource; and access to the Employee Assistance Program providing 24/7 confidential assessment counseling and referral services. Retirement benefits include the GE Vernova Retirement Savings Plan a tax-advantaged 401(k) savings opportunity with company matching contributions and company retirement contributions as well as access to Fidelity resources and financial planning consultants. Other benefits include tuition assistance adoption assistance paid parental leave disability benefits life insurance 12 paid holidays and permissive time off.

GE Vernova Inc. or its affiliates (collectively or individually GE Vernova) sponsor certain employee benefit plans or programs GE Vernova reserves the right to terminate amend suspend replace or modify its benefit plans and programs at any time and for any reason in its sole discretion. No individual has a vested right to any benefit under a GE Vernova welfare benefit plan or program. This document does not create a contract of employment with any individual.