CLIENT HIGHLIGHT
The client you'll be supporting is a Fortune 500 global leader in energy technology, focused on helping the world produce cleaner, more reliable power. Their teams design and improve the systems that keep homes, businesses, and communities running, from gas and wind turbines to the electrical grids that connect them. This is a chance to be part of a company that's driving innovation, supporting sustainability, and shaping the future of energy.
LOCATION
Greenville, South Carolina, 29615
COMPENSATION
$47-53 per hour, full benefits offered
SCHEDULE
Hybrid; local candidates
Standard Hours: 40 hours per week
CONTRACT TERM
1 year with high likelihood of extension or conversion
POSITION OVERVIEW
The Data & Analytics (D&A) Developer II / Data Scientist supports the HDPE Operations & Strategy team, serving as the bridge between engineering domain knowledge, business operations, and IT execution. This role defines data requirements, builds AI/ML and scenario-planning models, and delivers harmonized insights and reporting to business stakeholders worldwide. Responsibilities: Analyze data across enterprise systems (SAP, Salesforce, Databricks, Power BI); develop and validate machine learning models for forecasting and scenario planning; build and maintain Python-based data pipelines; track project execution through P6 and other project systems; reverse-engineer existing dashboards and SQL logic; and translate technical findings into actionable business insights.
RESPONSIBILITIES
Data Analysis & Intelligence
- Analyze data from multiple enterprise systems (SAP, Salesforce, Databricks, Power BI, labor and finance systems) to identify patterns, gaps, and improvement opportunities
- Work with Program Managers and Operations leaders to define relevant data assets and specify how data should be accessed, interpreted, and used
- Transform large structured/unstructured datasets (100k+ rows) into actionable insights
- Conduct data quality checks and resolve data defects across enterprise platforms
AI/ML Model Development & Deployment
- Develop and validate machine learning models supporting demand forecasting and scenario modeling
- Document analytical findings, model performance, and data definitions for transparency and reproducibility
- Build and maintain Python-based data pipelines for ETL, model training, and automated forecasting workflows in collaboration with Data Engineers
- Translate business data challenges into concrete data science and AI/ML problem statements
- Leverage LLMs and prompt engineering to build tools that augment decision-making and automate workflows
Scenario Planning & Project Execution Analytics
- Design and execute scenario planning models to test business assumptions and evaluate what-if outcomes
- Track project execution data across P6 (Primavera) and other systems, linking planning assumptions to actual performance
- Support variance analysis between planned assumptions and actual execution to identify gaps and trends
- Build automated tracking solutions monitoring assumption validity through project lifecycle stages
- Collaborate with Program Managers to refine planning assumptions based on execution learnings
- Provide data pipelines and data to build executive dashboards visualizing assumption-to-execution alignment
Existing Data Ecosystem & Optimization
- Review existing dashboards, models, and data pipelines to understand design patterns and data flows
- Read and interpret SQL queries and business logic embedded in current reports and analytical systems
- Identify opportunities to optimize or consolidate existing reporting and modeling assets
- Maintain consistency with established data standards and best practices
Business Stakeholder Collaboration & Continuous Improvement
- Translate complex data findings and model outputs into clear, actionable insights for technical and non-technical audiences
- Support centralized, KPI-based reporting solutions for business stakeholders across global business lines
- Collaborate with Data Analysts and Data Engineers to ensure data requirements are implemented correctly at the pipeline and infrastructure level
- Stay current with AI, ML, and data science advancements, proposing new approaches to enhance solutions
REQUIREMENTS
- Bachelor's degree in Data Science, Computer Science, Engineering, or related field.
- Strong proficiency in Python (pandas, numpy, scikit-learn, scipy) and SQL.
- Experience with statistical modeling, scenario/what-if analysis, and model validation.
- Familiarity with enterprise data systems (SAP, Salesforce, Databricks) and LLM/prompt engineering concepts.
- Strong communication skills and ability to work across international, multicultural teams.
