Data Scientist

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 maintenance and enhancement
Identify opportunities to optimize or consolidate existing reporting and modeling assets
Maintain consistency with established GE Vernova data standards and best practices
Business Stakeholder Collaboration

Translate complex data findings and model outputs into clear, actionable business insights for both technical and non-technical audiences
Resolve customer and internal user queries related to model outputs, data insights, or data defects
Support the Operations team in delivering centralized data analysis-based reporting solutions (including KPI), providing harmonized insights and KPIs to business stakeholders across GE Vernova's global business lines 
Innovation & Continuous Improvement

Collaborate closely with cross-functional Data analysts and Data engineers to ensure data requirements are correctly understood and implemented at pipeline and infrastructure level
Build and maintain a deep understanding of Semantic Data Models to ensure consistent data interpretation across applications and business systems
Stay current with the latest advancements in AI, ML, and data science, proactively proposing new approaches that could enhance our solutions
Contribute to the evolution of Engineering Data Quality, bringing innovative ideas and a forward-thinking mindset to continuously improve our modeling and tooling landscape
Essential Soft Skills & Competencies

Communication & Collaboration

Stakeholder interaction skills: Ability to engage with non-technical audiences and translate complex technical concepts and AI/ML findings into business value
Understanding & listening skills: Proven ability to grasp business requirements, ask clarifying questions, and define clear data requirements for distributed execution teams
Positive communication style: Professional, proactive, and solution-oriented approach
Multilingual capability: Fluent in English (written and spoken); additional languages are a plus