Lead Data Scientist, Predictive Fleet Analytics

GE Vernova, Inc.; Greenville (SC); On-site; USD 113,000 - 189,000; Full time

Benefits offered by this job

Medical/Dental/Vision

Health Coach

Employee Assistance Program

401(k) with company match

Tuition assistance

Parental leave

Job summary

GE Vernova, Inc. seeks a Senior Data Scientist for Blade Fleet Engineering to lead proactive fleet performance management through advanced data science and ML. You will drive scalable analytical initiatives, translating complex data into actionable improvements across the product lifecycle.

You will collaborate with cross-functional teams to deliver reliable insights, build predictive models, and continuously improve data quality and analytics infrastructure to support fleet reliability goals.

Qualifications

• 7+ years of professional data science experience.
• Experience with predictive analytics in industrial settings.
• Strong data visualization and storytelling for stakeholders.

Responsibilities

• Integrate predictive analytics across fleet operations to anticipate degradation.
• Develop ML models to process large historical failure data.
• Create automated dashboards to monitor fleet quality KPIs.
• Collaborate with cross-functional teams on data-driven reliability improvements.
• Own data infrastructure improvements for analytics capabilities.

Skills

Data Science

Predictive Analytics

Machine Learning

Data Visualization

SQL

Python

R

Education

Bachelor's degree in data science or related field

Tools

PowerBI

Tableau

MATLAB

Job description

GE Vernova, Inc. seeks a Senior Data Scientist for Blade Fleet Engineering to lead proactive fleet performance management through advanced data science and ML. You will drive scalable analytical initiatives, translating complex data into actionable improvements across the product lifecycle.

You will collaborate with cross-functional teams to deliver reliable insights, build predictive models, and continuously improve data quality and analytics infrastructure to support fleet reliability goals.