we are accelerating the path toward a more reliable, affordable, and sustainable energy future. Our teams help customers power economies and deliver electricity that supports health, safety, security, and quality of life worldwide.
We are looking for a curious, analytical, and digitally passionate Data Scientist to join our HDPE Operations & Strategy team — a team where collaboration, innovation, and participative leadership drive everything we do.
This is an opportunity to create meaningful impact from day one by applying data intelligence, AI, and machine learning to transform how we plan, predict, and operate across GE Vernova’s global business.

As a Data Scientist, you will serve as a critical bridge between:
✅ Engineering domain expertise
✅ Business operations & planning
✅ Data engineering & IT execution teams
You will define data requirements, develop AI/ML solutions, build predictive models, and deliver actionable insights that enable smarter business decisions.
You will support centralized operations reporting, scenario planning, forecasting, and program execution analytics to help stakeholders understand performance, predict outcomes, and proactively address risks.

Key Responsibilities
Data Analysis & Business Intelligence
• Analyze enterprise data from systems such as SAP, Salesforce, Databricks, Power BI, finance, and labor platforms to identify insights and improvement opportunities
• Transform large structured and unstructured datasets into actionable business recommendations
• Perform data quality analysis, identify anomalies, and resolve data inconsistencies
• Partner with business leaders to define relevant data assets and usage strategies

AI/ML Model Development
• Develop and validate machine learning models for forecasting, predictive analytics, and scenario-based decision-making
• Build Python-based data pipelines for ETL, model training, and automated analytics workflows
• Translate business challenges into AI/ML problem statements and scalable solutions
• Apply Large Language Models (LLMs) and prompt engineering techniques to create intelligent business tools
• Document model performance, analytical findings, and data definitions for transparency and reuse

Scenario Planning & Project Execution Analytics
• Design “what-if” scenario models to evaluate demand, capacity, cost, and resource planning assumptions
• Analyze project execution data from systems such as P6 (Primavera) and enterprise platforms
• Identify gaps between planned assumptions and actual execution performance
• Develop automated tracking solutions to monitor project lifecycle performance
• Provide insights through executive dashboards highlighting risks and opportunities

Data Ecosystem Optimization
• Review existing dashboards, models, SQL logic, and data pipelines to understand business requirements and data flows
• Analyze semantic data models and improve reporting consistency
• Identify opportunities to optimize existing analytical solutions and reporting assets
• Collaborate with Data Engineers to ensure successful implementation of data requirements

Business Collaboration & Innovation
• Translate complex analytical outputs into clear business insights for technical and non-technical audiences
• Support KPI reporting and centralized analytics solutions across GE Vernova business lines
• Collaborate with Data Analysts, Engineers, and Operations teams to continuously improve data quality and analytics capabilities
• Stay current with emerging AI/ML technologies and propose innovative solutions

Required Technical Skills
Data Science & Machine Learning
✔ Strong Python skills (pandas, numpy, scipy, scikit-learn, statistical modeling, OOP)
✔ Experience with scenario planning and what-if analysis
✔ Knowledge of machine learning methodologies and frameworks (scikit-learn, XGBoost, or similar)
✔ Understanding of model evaluation metrics (R², MAE, RMSE, cross-validation)
✔ Strong SQL skills including complex queries, joins, and data manipulation
✔ Knowledge of statistical analysis, hypothesis testing, and experimental design
Data Management & Analytics
✔ Enterprise data exploration and pattern identification
✔ Data cleaning and standardization across multiple systems
✔ Data integration from ERP/CRM platforms and enterprise sources
✔ Experience identifying anomalies and data quality issues
AI & Advanced Analytics
✔ Understanding of semantic data models
✔ Experience developing forecasting and predictive models
✔ Familiarity with LLMs and prompt engineering concepts
Dashboard & Data Logic Understanding
✔ Ability to reverse engineer dashboards, reports, and analytical models
✔ Strong SQL and business logic interpretation skills
✔ Understanding of data lineage and source systems
✔ Experience collaborating with Data Engineering teams

Preferred Qualifications
• Experience with TensorFlow, PyTorch, neural networks, or deep learning
• Knowledge of unit testing frameworks such as pytest
• Experience with Primavera P6, MS Project, or project execution systems
• Familiarity with MLOps practices (MLflow, experiment tracking, model versioning)
• Experience with cloud platforms (Azure, AWS, GCP)
• Advanced LLM experience including RAG, fine-tuning, or AI agent frameworks
• Understanding of data governance and responsible AI practices
• Experience consuming enterprise data from SAP, Salesforce, Databricks, or similar platforms

What We’re Looking For
Communication & Collaboration
• Ability to explain complex technical concepts and AI/ML insights in business terms
• Strong stakeholder engagement and requirement-gathering skills
• Professional, proactive, and solution-oriented communication style
• Fluent English communication skills; additional languages are a plus

Mindset & Work Style
• Strong analytical thinking and problem-solving ability
• Curiosity to understand existing systems, models, and processes
• Ability to collaborate across global, multicultural teams
• Passion for continuous learning in AI, ML, and data science
• Ownership mindset with proactive communication of progress, risks, and opportunities

Data Scientist

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