Lead Engineer - Fleet Model job opportunity at GE Aerospace.

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DatePosted 25 Days Ago bot
GE Aerospace Lead Engineer - Fleet Model
Experience: 5-years
Pattern: full-time
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loacation Bengaluru, India
loacation Bengaluru....India

Job Description Summary The Lead Engineer– Fleet Model supports the continuous CSA contract process for GE9X and other assigned fleets. You will contribute to Time-on-Wing (TOW) and cost modeling for CSA engines, assist cross-functional productivity projects with Services partners (LCE, Systems, CSA CMR Operations, IT), and help turn large, complex field datasets into actionable insights on engine field capability. This role builds strong technical foundations while providing exposure to the commercial side of aviation services and meaningful opportunities to improve Safety, Quality, Delivery, and Cost. Job Description Company Overview Working at GE Aerospace means you are bringing your unique perspective, innovative spirit, drive, and curiosity to a collaborative and diverse team working to advance aerospace for future generations. If you have ideas, we will listen. Join us and see your ideas take flight! Site Overview Established in 2000, the John F. Welch Technology Center (JFWTC) in Bengaluru is GE Aerospace’s multidisciplinary research and engineering center. Pushing the boundaries of innovation every day, engineers and scientists at JFWTC have contributed to hundreds of aviation patents, pioneering breakthroughs in engine technologies, advanced materials, and additive manufacturing.    Role Overview: 1. Support fleet analyses for CSA decisions by preparing inputs, running scenarios, and summarizing insights under senior guidance. 2. Contribute to development and updation of Fleet models (FMs) and product field capability models using field, shop, and test cell data. 3. Assist in generating reliability, availability, and maintainability (RAM) predictions using defined probabilistic methods and templates. 4. Perform data readiness tasks: sourcing, cleaning, and basic QC checks to durability and cost modeling datasets. 5. Apply statistical analyses and entry-level ML techniques (e.g., regression, clustering) to explore TOW and SV cost drivers with oversight. 6. Document methodologies, assumptions, parameters, results, and validations in compliance with engineering policies and standard work. 7. Participate in cross-functional productivity projects (LCE, Systems, CSA CMR Ops) focused on SV cost reduction and TOW improvement. 8. Build and maintain reproducible analysis scripts and dashboards (e.g., Python, SQL, Excel) to streamline model runs and reporting. 9. Track risks, and dependencies; provide clear, timely status updates and escalate blockers to ensure on-time delivery. 10. Engage stakeholders to clarify requirements, review findings, and translate technical outcomes into business-relevant recommendations. 11. Adhere to safety, data governance, and security practices; proactively flag data quality or model risk issues impacting decisions. 12. Contribute to continuous improvement by capturing lessons learned, improving templates/checklists, and participating in code/model reviews. Required Qualifications: Bachelor’s degree in engineering (e.g., Mechanical, Aerospace, Industrial, Electrical, Computer, or related) Minimum of 5 years of relevant experience in aviation, gas turbine, or related engineering domains (design, analysis, systems, data/analytics, or services), or equivalent academic/project experience Strong analytical and problem-solving skills; ability to work with datasets and generate actionable insights Proficiency with engineering and data tools (e.g., Excel, Python/R) and willingness to learn specialized tools Effective written and verbal communication skills; ability to present findings to technical and non-technical audiences Demonstrated ability to work in teams and in a matrixed environment Preferred Qualifications: Master’s degree in Engineering or relevant discipline Strong technical aptitude, including applicable engineering tools and systems Exposure to CSA or services economics, life-cycle cost, or reliability engineering concepts Experience with large datasets, statistical analysis (regression, classification, clustering) Familiarity with probabilistic methods and RAM modeling Curiosity, bias for action, and continuous improvement mindset; comfort with ambiguity and iterative problem-solving Organizational skills with attention to detail and ability to manage multiple tasks Additional Information: Whether we are manufacturing components for our engines, driving innovation in fuel and noise reduction, or unlocking new opportunities to grow and deliver more productivity, our GE Aerospace teams are dedicated and making a global impact. Join us and help move the aerospace industry forward #LI-AM4 Additional Information Relocation Assistance Provided: No

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