Energy Forecasting & Load Research Intern

Xcel Energy; Denver (CO); Hybrid; USD 27,000 - 33,000; Part time

Benefits offered by this job

Pension

401(k) plan

Paid time off

Holidays

Job summary

Xcel Energy is seeking interns to support CFO Load Research and Energy Forecasting teams, contributing to forecasts, data analyses, and modeling efforts that inform planning and regulatory activities.

Ideal candidates are pursuing quantitative degrees, able to work summer full-time or school-year part-time, and comfortable with Python, SQL, and Excel in a collaborative, hybrid environment.

Qualifications

• Pursuing a quantitative degree at an accredited college or university.
• Must be able to work full-time during summer (up to 40 hours/week) or part-time during the school year (up to 20 hours/week) in-person per hybrid policy.
• Strong organization, communication, and problem-solving skills.

Responsibilities

• Support load research and forecasting projects by gathering, reviewing, and summarizing data.
• Assist with analysis used for planning, budgeting, regulatory, and business decisions.
• Help improve tools, reports, and processes for recurring forecasting and research work.
• Explore how customer behavior, weather, and new technologies may affect energy use.
• Learn forecasting concepts and contribute to projects that strengthen forecasting capabilities.
• Document work and share findings with team members and stakeholders.

Skills

Python (pandas)

R

SQL

Databricks

Education

Current Student, Junior status (as of fall 2027) or higher

Pursuing a degree in Statistics, Data Science, Economics/Econometrics, Applied Mathematics, Mathematics, Electrical Engineering, Computer Engineering, Computer Science, Environmental Studies, Public Policy, or related quantitative field

Tools

Microsoft Excel

Databricks

Job description

Xcel Energy is seeking interns to support CFO Load Research and Energy Forecasting teams, contributing to forecasts, data analyses, and modeling efforts that inform planning and regulatory activities.

Ideal candidates are pursuing quantitative degrees, able to work summer full-time or school-year part-time, and comfortable with Python, SQL, and Excel in a collaborative, hybrid environment.