Proactive Fleet Analytics Lead

A01098 GE Vernova International LLC; South Carolina; On-site; USD 113,000 - 189,000; Full time

Benefits offered by this job

Medical coverage

Dental coverage

Vision coverage

Prescription drug coverage

401(k) plan with match

Tuition assistance

Adoption assistance

Paid parental leave

Disability benefits

Life insurance

12 paid holidays

Job summary

GE Vernova is seeking a Senior Data Scientist, Blade Fleet Engineering, to lead proactive fleet performance management through advanced data science and machine learning.

You will develop scalable analytics initiatives and production ML models, drive data-driven improvements across the product lifecycle with cross-functional teams worldwide, and use large-scale industrial data to identify early degradation and optimize maintenance strategies.

Qualifications

• Bachelor's degree in a quantitative STEM field.
• 7+ years of professional experience in data science or similar role.
• Proficient in data analysis and visualization tools.

Responsibilities

• Lead predictive analytics with fleet-wide data to identify degradation patterns.
• Develop and deploy ML models for prognosis and maintenance.
• Create automated dashboards to monitor key KPIs.
• Collaborate with cross-functional teams to improve fleet reliability.
• Drive data-driven problem solving and RCA with statistical methods.

Skills

Data analysis

Visualization

SQL

Python

R

MATLAB

PowerBI

Tableau

AI/ML

Statistical analysis

Quality systems

Communication

Lean methodologies

Education

Bachelor's degree in Data Science/CS/Statistics/Math

Tools

SQL

Python

R

MATLAB

PowerBI

Tableau

Job description

GE Vernova is seeking a Senior Data Scientist, Blade Fleet Engineering, to lead proactive fleet performance management through advanced data science and machine learning.

You will develop scalable analytics initiatives and production ML models, drive data-driven improvements across the product lifecycle with cross-functional teams worldwide, and use large-scale industrial data to identify early degradation and optimize maintenance strategies.