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

Qualifications & Requirements

• •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.

Desired Characteristics

• •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).

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 authorized to work in the United States for this opening. Any offer of employment is conditioned upon the successful completion of a drug screen (as applicable).

Relocation Assistance Provided: Yes

For candidates applying to a U.S. based position, the pay range for this position is between $113,200.00 and $188,800.00. The Company pays a geographic differential of 110%, 120% or 130% of salary in certain areas. The specific pay offered may be influenced by a variety of factors, including the candidate’s experience, education, and skill set.

Bonus eligibility: discretionary annual bonus.

This posting is expected to remain open for at least seven days after it was posted on September 25, 2026.

Available benefits include medical, dental, vision, and prescription drug coverage; access to Health Coach from GE Vernova, a 24/7 nurse-based resource; and access to the Employee Assistance Program, providing 24/7 confidential assessment, counseling and referral services. Retirement benefits include the GE Vernova Retirement Savings Plan, a tax-advantaged 401(k) savings opportunity with company matching contributions and company retirement contributions, as well as access to Fidelity resources and financial planning consultants. Other benefits include tuition assistance, adoption assistance, paid parental leave, disability benefits, life insurance, 12 paid holidays, and permissive time off.

GE Vernova Inc. or its affiliates (collectively or individually, “GE Vernova”) sponsor certain employee benefit plans or programs GE Vernova reserves the right to terminate, amend, suspend, replace, or modify its benefit plans and programs at any time and for any reason, in its sole discretion. No individual has a vested right to any benefit under a GE Vernova welfare benefit plan or program. This document does not create a contract of employment with any individual.