Job Description Summary

Job Description

Essential Responsibilities

• 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.
• 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 iniatives driven by cross funstional teams such as Blade FAX
• Team Work: Proven ability to work effectively in a globally focused, culturally diverse, and highly matrixed organizational environment.

Qualifications & Requirements

• Education: Bachelor’s 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.

Desired Characteristics

• 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.

Additional Information