Project Overview: Developing and refining enterprise data models that integrate commercial, operational, and financial data Building machine learning–based electrical power outlook forecasts (demand, pricing, and generation patterns) Supporting margin forecasting models to improve profitability insights and decision-making. Internship Outcomes: Contribute to an enterprise data model that connects customer, operations, and financial data for analytics use cases Prototype and evaluate one or more ML models for electrical power outlook forecasting (e.g., load, price, or renewable generation) Assist in enhancing or developing a margin forecasting model (e.g., at deal, product, or portfolio level) with clear performance metrics Document data definitions, model assumptions, and analytical workflows for handoff to the broader team. Primary Skills Developed: Enterprise Data Modeling: Conceptual, logical, and/or physical data modeling Understanding of relational databases, data warehousing concepts, and dimensional modeling Machine Learning & Forecasting: Time series analysis and regression techniques Model evaluation (e.g., MAE, RMSE, MAPE) Analytics & Visualization: Data exploration, feature engineering, and basic visualization to explain model behavior Technical Tools (typical examples): Programming: Python or R Data: SQL, familiarity with data warehouses or cloud data platforms ML/Analytics libraries: pandas, scikit-learn, statsmodels (or equivalents)
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Career Level
Intern