Job at a glance
Job Description Summary The Availability & Reliability Engineer is a specialized engineering role within the Wind Engineering division. This is not a theoretical data science position; it is a hardware-focused role for engineers who use data as a critical tool to inform the physical design and maintenance of wind turbines. Your mission is to serve as the architect of turbine reliability modeling, applying mechanical and systems engineering principles to massive fleet datasets.
You will transform raw performance data into actionable reliability models and high-fidelity availability forecasts that directly inform the design of more durable hardware, optimize service contracts, and set customer performance expectations. Job Description Roles and Responsibilities Failure Analysis: Ingest and analyze performance data to identify the physical drivers of turbine unavailability.
You must be able to distinguish between technical component failures, turbine/component design architecture, and manufacturing differences. Probabilistic Modeling: Develop component-level reliability models to predict the life cycle of critical turbine systems (e.g., drivetrains, bearings, blades) utilizing statistical analysis methods. Design Verification: Partner with Design Engineering teams to set reliability targets for new products.
Verify that replacement parts meet or exceed the performance of the original components. Infrastructure Management: Own the data pipeline for statistical analysis methods. Manage data governance, execute data pulls, and ensure high-fidelity reporting for critical asset dashboards (e.g., Main Bearing performance). Inventory Optimization: Use data-driven projections to forecast spare part demand, balancing inventory levels with the need for immediate turbine return-to-service.
Technical Forecasting: Build statistical forecasts for fleet-wide availability to support Long-Term Service Agreements (LTSA) and financial risk management. Hardware Simulation: Model the expected performance "lift" of proposed fleet retrofits or design changes before capital is deployed. Required Qualifications Bachelor’s degree in Mechanical Engineering, Electrical Engineering, or a closely related engineering field (e.g., Systems Engineering).
At least 3 years of professional or internship experience in an industrial setting, preferably in wind energy, power generation, or heavy-duty machinery. At least 1 year’s professional or internship experience in probability and statistics as applied to physical engineering systems. Desired Characteristics Candidates with a pure Data Science or Statistics degree must demonstrate substantial industrial/mechanical engineering experience.
Engineering Application of Data: Proven ability to apply software (Python, JMP/JSL, R) and query languages (SQL, Databricks, Redshift) to solve physical engineering problems. We are looking for engineers who use code as an enabler to improve hardware, rather than generalist data scientists focusing on information architecture. Domain Knowledge: Demonstrated understanding of physical failure modes, asset lifecycle management, and mechanical nuances of rotating machinery.
Communication: Ability to bridge the gap between complex engineering data and high-level stakeholders, distilling technical findings into clear, actionable conclusions for the business. Program Capability: Exposure to programmatic tool usage such as Gantt charts and the demonstrated ability to self-plan and execute work scope with limited oversight 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 a