Senior Engineer, Blades Fleet Engineering

Predictive Analytics Integration: Leverage fleet-wide data across manufacturing projects and services platforms alongside AI-driven diagnostic tools to identify early-stage blade degradation enabling proactive maintenance.

Data-Driven RCA Methodology: Utilize machine learning algorithms to process large-scale historical failure data accelerating root cause identification and validating the efficacy of corrective actions through statistical modeling.

Automated Quality Reporting: Implement and maintain automated data visualization dashboards to monitor Blade fleet quality KPIs ensuring real-time visibility into emerging trends for leadership and stakeholders.

• Blade Master file: Create and maintain Blade master file database for all GE Vernova Wind blades integrating existing database from Production Installation and Service

Proactive Fleet Management: Collaborate with blade fleet performance teams to identify and address emerging technical issues before they impact fleet availability.

Data Infrastructure Ownership: Drive continuous improvements in fleet data quality data completeness and the underlying infrastructure supporting our analytics.

Problem-Solving Leadership: Facilitate RCA Kaizens to achieve faster more robust resolutions. Own and support action items derived from Quality PSR countermeasures.

Process Implementation: Drive the application of structured problem-solving tools and methods throughout the entire RCA process.

Cross-Functional Partnership: Seamlessly collaborate with fleet performance management manufacturing projects services and digital technology teams to ensure a unified approach to fleet reliability.

• Support similar initiatives driven by cross functional teams such as being focal point within Blade Fleet engineering for Blade FAX and Turbine monitoring platforms

Team Work: Proven ability to work effectively in a globally focused culturally diverse and highly matrixed organizational environment.

Education: Bachelors degree in Engineering (STEM-based).

Experience: 7 years of professional experience in wind turbine blade engineering and/or product management.

Travel: Ability to travel globally approximately 10% of the time.

Data Literacy & Tooling: Proficiency in data analysis and visualization software (e.g. SQL Python R MATLAB PowerBI or Tableau) with experience interpreting large complex datasets for technical decision-making.

AI/ML Expertise: Understanding of how AI and predictive maintenance models apply to mechanical structures and wind turbine blade health monitoring.

Statistical Analysis: Strong foundation in statistical quality control (SQC) and reliability engineering metrics (e.g. Weibull analysis reliability growth modeling).

Risk Mitigation: Experience developing comprehensive action plans to mitigate fleet risks arising from technical issues.

Quality Systems: Familiarity with quality systems procedure development technical training and execution.

Communication: Experience with high-level customer communications regarding quality and RCA outcomes.

Lean Methodologies: Proficiency in Lean tools coaching and facilitating Kaizen events.