Data & Analytics (D&A) Developer II (2985)

Location: Greenville, SC, US (Hybrid)
Openings: 1
Job Type: Contract
Duration: 1 Year
Rate: $47-52/hour W2 + benefits
Hours: 40 hours/week 
Project: Gas Turbines

We are seeking a curious, analytically sharp, and digitally passionate Data Scientist to join our HDPE Operations & Strategy team - a team where collaboration and participative leadership are not just words, but the way we work every day. This is your opportunity to create real impact from day one. As a core member of our HDPE team, you will be at the forefront of our engineering vision — where data intelligence and AI-powered tools redefine how we manage, predict, and operate across GE Vernova’s global business.

You will act as the critical bridge between our Engineering domain data knowledge, business planning, operations and our IT execution team — defining what data we need, how it should be structured and used, and what AI/ML solutions can unlock the most value. You will support centralized business operations and program reporting that delivers harmonized insights and predicted range of outcomes to business stakeholders worldwide.

You will build scenario planning models that test critical business assumptions and track project execution through P6 and enterprise systems, identifying gaps between plan and reality to drive proactive decision-making. This role will be critical in efforts to optimize HDPE Operations program management activities

Required Technical Skills

Core Data Science & ML Tools

Python: Strong proficiency in data analysis, statistical modeling, and ML development (pandas, numpy, scikit-learn, scipy, curve fitting, object-oriented programming)
Scenario Planning & What-If Analysis: Ability to build multi-scenario models to test assumptions and evaluate alternative planning outcomes
Machine Learning: Foundational to intermediate experience with ML frameworks and methodologies (scikit-learn, XGBoost, or similar)
Model Evaluation: Understanding of model validation metrics (R², MAE, RMSE, cross-validation, custom scoring functions)
SQL: Proficiency in querying, joining tables, data manipulation, and interpreting complex queries
Statistical Analysis: Understanding of statistical modeling, hypothesis testing, and experimental design

Data Management Competencies

Data Exploration: Ability to independently explore enterprise datasets and identify patterns, gaps, and opportunities
Data Cleaning: Experience handling messy data, identifying inconsistencies, and standardizing formats across heterogeneous systems
Data Integration: Experience merging multiple datasets from various enterprise data sources (SAP, Salesforce, Databricks, ERP/CRM)
Anomaly Detection: Sharp eye for finding outliers, errors, and unusual patterns in structured and unstructured data

AI & Advanced Analytics

Semantic Data Models: Understanding of data modeling concepts across heterogeneous systems
Forecasting & Prediction: Experience developing models for scenario modeling and predictive use cases
Large Language Models (LLMs): Familiarity with LLMs and basic prompt engineering techniques for practical business applications

Dashboard & Logic Comprehension

Reverse Engineering: Ability to review existing dashboards, ML models, and reports to understand design patterns, business requirements, and underlying data sources
SQL Query Analysis: Strong capability to read and interpret complex SQL queries to understand data flows and business logic
Data Source Understanding: Skills to trace data lineage, review prepared data sources, and comprehend underlying data structures
Pipeline Collaboration: Experience working with Data Engineers to ensure data requirements are correctly implemented at pipeline and infrastructure level

Nice to Have Skills

Advanced ML/Deep Learning: Experience with TensorFlow, PyTorch, neural networks, or deep learning applications
Unit Testing: pytest or similar frameworks for data science code quality
Experience with P6 (Primavera), MS Project, or similar project execution systems
MLOps: Model versioning, experiment tracking (MLflow, Weights & Biases), deployment basics
Cloud Platforms: Familiarity with Azure, AWS, or GCP for data science workflows
Advanced LLM Applications: Experience with fine-tuning, RAG (Retrieval-Augmented Generation), or agent frameworks
Data Governance: Understanding of data governance principles and responsible AI practices
Enterprise Systems: First-hand experience with SAP, Salesforce, Databricks, or similar ERP/CRM systems from a data consumption perspective

Key Responsibilities

Data Analysis & Intelligence

Analyze quality data from multiple enterprise systems (SAP, Salesforce, Databricks, Power BI, labor systems, finance data) to identify patterns, gaps, and opportunities for data-driven improvements
Work with Program Managers and/or Operations leaders to define which data assets are relevant for business use cases and specify how data from different systems should be accessed, interpreted, and used
Transform structured/unstructured datasets (often 100k+ rows) into actionable insights
Conduct data quality checks and identify/resolve data defects and abnormalities across enterprise platforms

AI/ML Model Development & Deployment

Develop and validate Machine Learning models that support demand forecasting, scenario modeling, and predictive use cases for short-term and long-term business goals
Document analytical findings, model performance, and data definitions clearly to ensure transparency and reproducibility across the team
Pipeline Collaboration & Development: Experience working with Data Engineers to ensure data requirements are correctly implemented; ability to build and maintain Python-based data pipelines for ETL, model training, and automated forecasting workflows
Translate business technical data challenges into concrete data science and AI/ML problem statements, acting as the domain-aware bridge between Engineering/Operations and the Digital team
Leverage Large Language Models (LLMs) and prompt engineering to build intelligent tools that augment human decision-making and automate workflows

Scenario Planning & Project Execution Analytics

Design and execute scenario planning models to test business assumptions (demand forecasts, resource capacity, cost projections) and evaluate “what-if” outcomes for strategic decision-making
Track project execution data across P6 (Primavera) and other project management systems, linking planning assumptions to actual execution performance
Support variance analysis between planned assumptions (forecast hours, budgets, timelines) and actual project execution data to identify gaps, root causes, and trends
Build automated tracking solutions that monitor assumption validity as projects progress through lifecycle stages (planning → design -> execution → closeout)
Collaborate with Program Managers to refine planning assumptions based on execution learnings and historical pattern analysis
Provide data pipeline and data to build executive dashboards that visualize assumption-to-execution alignment, highlighting projects at risk due to assumption breakdown

Existing Data Ecosystem & Optimization

Review and analyze existing GE Vernova dashboards, models, and data pipelines to understand design patterns, business requirements, and data flows
Read and interpret SQL queries, business logic, and semantic models embedded in current reports and analytical systems
Understand underlying data structures and prepared data sources to support