Lead Informatics Engineer

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.

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:

• Bachelor’s, Master’s, 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.

GE Vernova offers a great work environment, professional development, challenging careers, and competitive compensation.

GE Vernova is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law.

GE Vernova will only employ those who are legally authorized to work in the United States for this opening. Any offer of employment is conditioned upon the successful completion of a drug screen (as applicable).

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