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

Location: Greenville, SC, US (Hybrid)Openings: 1Job Type: ContractDuration: 1 YearRate: $47-52/hour W2 + benefitsHours: 40 hours/week Project: Gas TurbinesWe 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 activitiesRequired Technical SkillsCore Data Science & ML ToolsPython: 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 outcomesMachine 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 queriesStatistical Analysis: Understanding of statistical modeling, hypothesis testing, and experimental designData Management CompetenciesData Exploration: Ability to independently explore enterprise datasets and identify patterns, gaps, and opportunitiesData Cleaning: Experience handling messy data, identifying inconsistencies, and standardizing formats across heterogeneous systemsData 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 dataAI & Advanced AnalyticsSemantic Data Models: Understanding of data modeling concepts across heterogeneous systemsForecasting & Prediction: Experience developing models for scenario modeling and predictive use casesLarge Language Models (LLMs): Familiarity with LLMs and basic prompt engineering techniques for practical business applicationsDashboard & Logic ComprehensionReverse Engineering: Ability to review existing dashboards, ML models, and reports to understand design patterns, business requirements, and underlying data sourcesSQL Query Analysis: Strong capability to read and interpret complex SQL queries to understand data flows and business logicData Source Understanding: Skills to trace data lineage, review prepared data sources, and comprehend underlying data structuresPipeline Collaboration: Experience working with Data Engineers to ensure data requirements are correctly implemented at pipeline and infrastructure levelNice to Have SkillsAdvanced ML/Deep Learning: Experience with TensorFlow, PyTorch, neural networks, or deep learning applicationsUnit Testing: pytest or similar frameworks for data science code qualityExperience with P6 (Primavera), MS Project, or similar project execution systemsMLOps: Model versioning, experiment tracking (MLflow, Weights & Biases), deployment basicsCloud Platforms: Familiarity with Azure, AWS, or GCP for data science workflowsAdvanced LLM Applications: Experience with fine-tuning, RAG (Retrieval-Augmented Generation), or agent frameworksData Governance: Understanding of data governance principles and responsible AI practicesEnterprise Systems: First-hand experience with SAP, Salesforce, Databricks, or similar ERP/CRM systems from a data consumption perspectiveKey ResponsibilitiesData Analysis & IntelligenceAnalyze quality data from multiple enterprise systems (SAP, Salesforce, Databricks, Power BI, labor systems, finance data) to identify patterns, gaps, and opportunities for data-driven improvementsWork 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 usedTransform structured/unstructured datasets (often 100k+ rows) into actionable insightsConduct data quality checks and identify/resolve data defects and abnormalities across enterprise platformsAI/ML Model Development & DeploymentDevelop and validate Machine Learning models that support demand forecasting, scenario modeling, and predictive use cases for short-term and long-term business goalsDocument analytical findings, model performance, and data definitions clearly to ensure transparency and reproducibility across the teamPipeline 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 workflowsTranslate 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 teamLeverage Large Language Models (LLMs) and prompt engineering to build intelligent tools that augment human decision-making and automate workflowsScenario Planning & Project Execution AnalyticsDesign and execute scenario planning models to test business assumptions (demand forecasts, resource capacity, cost projections) and evaluate "what-if" outcomes for strategic decision-makingTrack project execution data across P6 (Primavera) and other project management systems, linking planning assumptions to actual execution performanceSupport variance analysis between planned assumptions (forecast hours, budgets, timelines) and actual project execution data to identify gaps, root causes, and trendsBuild 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 analysisProvide data pipeline and data to build executive dashboards that visualize assumption-to-execution alignment, highlighting projects at risk due to assumption breakdownExisting Data Ecosystem & OptimizationReview and analyze existing GE Vernova dashboards, models, and data pipelines to understand design patterns, business requirements, and data flowsRead and interpret SQL queries, business logic, and semantic models embedded in current reports and analytical systemsUnderstand underlying data structures and prepared data sources to support maintenance and enhancementIdentify opportunities to optimize or consolidate existing reporting and modeling assetsMaintain consistency with established GE Vernova data standards and best practicesBusiness Stakeholder CollaborationTranslate complex data findings and model outputs into clear, actionable business insights for both technical and non-technical audiencesResolve customer and internal user queries related to model outputs, data insights, or data defectsSupport the Operations team in delivering centralized data anal