Power Delivery Engineering Co-Op - Load FOrcasting & Analystics

Load Forecasting & Analytics Support Grid Reliability Through Forecasting, Data Science, and Advanced Analytics

Georgia System Operations Corporation (GSOC) is seeking a Load Forecasting & Analytics Co-Op to support Power Delivery Engineering. Load and solar forecasts are critical to GSOC operations, and advances in artificial intelligence, machine learning, and computing are creating new opportunities to expand internal forecasting capabilities.

As a Load Forecasting & Analytics Co-Op, you will work alongside engineering and analytics professionals to develop data workflows, support time-series forecasting, maintain vendor application interfaces, and improve the analytic processes used to forecast electric load and solar generation. Your work will help GSOC evaluate new approaches and strengthen the data foundation behind forecasting decisions.

This opportunity is ideal for an analytical and technically curious student who enjoys working with Python, data, predictive models, and complex business problems and wants to apply modern analytics to meaningful challenges in the electric utility industry.

As a Load Forecasting & Analytics Co-Op, you will support projects and recurring activities that contribute to forecasting accuracy, data quality, analytic capability, and continuous improvement.

• Develop and maintain data workflows that support load and solar forecasting.
• Collect, integrate, clean, and organize weather data, meter data, and other features used in time-series prediction.
• Assist with the development, evaluation, and refinement of machine learning and ensemble forecasting models.
• Support model testing, validation, performance monitoring, and documentation.
• Maintain and improve application programming interface (API) integrations with vendor forecasting applications.
• Develop repeatable analytic processes, reports, and tools that improve forecasting workflows and decision support.
• Use Python, Databricks, Excel, VBA, and other applicable tools to analyze data and automate recurring activities.
• Research emerging forecasting technologies and methods and summarize potential applications for team review.
• Present a summary of your projects, findings, recommendations, and key learnings at the conclusion of the assignment.

• Current enrollment in a bachelor's degree program in Engineering, Data Analytics, or a closely related quantitative field.
• Interest in load forecasting, solar forecasting, energy analytics, machine learning, predictive modeling, or time-series analysis.
• Experience using Python for data analysis, modeling, automation, or academic projects.
• Strong analytical and problem-solving skills, including the ability to organize data,identify patterns, and evaluate results.
• Attention to detail anda commitment to data quality, model documentation, and reproducible work.
• Clear written and verbal communication skills, including the ability to explain technical findings to professional audiences.
• Ability to work independently while collaborating effectively with engineering, analytics, and business partners.
• Strong organization and time-management skills, including the ability to manage assignments, meet deadlines, and communicate progress.
• A curious, accountable, and learning-oriented approach to unfamiliar data, tools, and forecasting challenges.

• Coursework, research, or project experience involving machine learning, time-series forecasting, predictive analytics, ensemble models, statistics, or data science.
• Experience preparing features from weather, meter, energy, or other time-series datasets.
• Interest in developing and comparing forecasting approaches to improve accuracy and business usefulness.

• Experience with Python, Databricks, Excel, or Visual Basic for Applications (VBA).
• Exposure to APIs, data integration, data workflows, automation, or vendor application interfaces.
• Ability to organize technical work, document methods, and produce repeatable analytic processes.

• Program Type:
  Co-Op.
• Expected Year:
  Rotation in 2027.
• Department:
  Power Delivery Engineering.
• Locatio…