Senior Data Scientist, Blades Fleet Engineering

Job Description Summary
The Senior Data Scientist, Blade Fleet Engineering for GE Vernova, will lead proactive fleet performance management through advanced data science and machine learning. You will be responsible for driving sustained product improvement across the global fleet, shifting from reactive troubleshooting to a proactive, data-led reliability model. You will develop and implement scalable analytical initiatives and drive closed-loop lessons learned across the product lifecycle by leveraging large-scale industrial data.

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 degradation patterns, enabling proactive maintenance strategies.
• Data-Driven RCA Methodology: Develop and deploy machine learning algorithms to process large-scale historical failure data, accelerating root cause identification and validating the efficacy of corrective actions through rigorous statistical modeling.
• Automated Quality Reporting: Implement and maintain automated data visualization dashboards and pipelines to monitor fleet quality KPIs, ensuring real-time visibility into emerging trends for leadership and stakeholders.
• Proactive Fleet Management: Collaborate with performance and reliability teams to identify and address emerging technical issues through advanced predictive modeling before they impact fleet availability.
• Data Infrastructure Ownership: Drive continuous improvements in fleet data quality, data completeness, and the underlying data architecture supporting our analytics capabilities.
• Problem-Solving Leadership: Facilitate data-driven "Kaizens" to achieve faster, more robust resolutions. Utilize statistical insights to own and support action items derived from Quality PSR (Problem Solving Report) countermeasures.
• Process Implementation: Drive the application of advanced data-centric problem-solving tools and methods throughout the root cause analysis process.
• Cross-Functional Partnership: Collaborate with fleet performance management, manufacturing, projects, services, and digital technology teams to ensure a unified, data-driven approach to fleet reliability. Support data initiatives driven by cross-functional teams

• Education: Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative STEM field.
• Experience: 7+ years of professional experience in data science, predictive analytics, or a similar high-impact technical analytical role.

• Data Literacy & Tooling: High proficiency in data analysis and visualization software (e.g., SQL, Python, R, MATLAB, PowerBI, or Tableau) with extensive experience interpreting large, complex datasets for technical decision-making.
• AI/ML Expertise: In-depth experience in building, deploying, and monitoring production-grade machine learning models, with an understanding of how predictive maintenance applies to complex mechanical structures.
• Statistical Analysis: Strong foundation in statistical quality control (SQC), probabilistic modeling, and reliability engineering metrics (e.g., Weibull analysis, reliability growth modeling).
• Risk Mitigation: Experience developing data-driven action plans to mitigate fleet risks.
• Quality Systems: Familiarity with quality systems, procedure development, and technical execution.
• Communication: Proven ability to communicate complex analytical findings and RCA outcomes to non-technical stakeholders.
• Lean Methodologies: Experience with Lean tools, coaching, and facilitating process improvement events (e.g., Kaizen).