How to apply for Analyst, Energy and Climate Modeling

About Energy Innovation

Energy Innovation promotes equitable energy policies to combat climate change and reduce emissions. The analyst role supports philanthropic grantmaking strategy, so the work directly informs where money goes in climate policy. This is a fit for someone who wants quantitative modeling to have a direct funding and policy impact.

About the role

You will modify, run, and maintain the Energy Policy Simulator and other energy models, design policy scenarios, input data, calibrate models, and identify bugs or propose fixes. You will also do advanced data analysis on large datasets, build quantitative models using AI agents, and turn findings into partner-facing reports, presentations, and graphics. The role supports philanthropic grantmaking strategy, so your modeling and research feed directly into funding decisions on energy and industrial policy.

A typical day

A typical day may involve modifying or running the Energy Policy Simulator, calibrating model inputs, and checking for bugs. You might also analyze large datasets with AI agents, draft a partner-facing report or graphic, and research a short-term policy request. The role supports philanthropic grantmaking strategy, so some days include turning model findings into materials for external partners.

Who Energy Innovation is looking for

• 4–6 years of experience in climate, energy, or environmental analysis, with hands-on work running and calibrating energy models.
• Advanced proficiency with Excel, AI agents, and data visualization tools, plus comfort handling large datasets.
• Understanding of energy models and policy design, including how to design scenarios and interpret emissions impacts of climate policy.
• Able to research energy and industrial policy, legislation, and regulations, and respond to short-term research requests from external partners.
• Willing to travel domestically at least twice yearly and work hybrid in San Francisco or Washington D.C. one to three days per week.

Tips for this application

• Show direct experience with the Energy Policy Simulator (EPS) or a comparable energy model. Name the model, what you modified or calibrated, and what policy scenarios you ran.
• Give concrete examples of using AI agents in quantitative modeling or data analysis. State which agents or tools, what task, and the result.
• Demonstrate that you can turn model output into partner-facing reports, presentations, and graphics. Include a sample or describe one deliverable you produced.
• Address the hybrid requirement directly. State your location and confirm you can be in the San Francisco or Washington D.C. office one to three days per week.
• Mention your experience with energy and industrial policy, legislation, and regulations. Show you can respond to short-term research requests, not just long modeling projects.

What to cover in your cover letter

Cover your hands-on work with the Energy Policy Simulator or a similar energy model, including scenario design, data input, calibration, and bug fixes. Explain how you use AI agents in quantitative modeling and large-dataset analysis. Describe how you have synthesized findings into partner-facing reports, presentations, and graphics. Show your grasp of energy and industrial policy and how modeling informs philanthropic grantmaking strategy.

Research before applying

• Read about the Energy Policy Simulator (EPS): what it models, its structure, and recent applications.
• Study Energy Innovation's work on equitable energy policies and how they support philanthropic grantmaking strategy.
• Review recent energy and industrial policy, legislation, and regulations in the US, especially those with emissions impact analysis.
• Check the hybrid office locations in San Francisco and Washington D.C. and confirm your ability to meet the one to three days per week requirement.

Likely interview topics

Based on the job description, expect questions about:

• Walk through a policy scenario you designed in an energy model. How did you input data, calibrate the model, and validate results?
• How have you used AI agents in quantitative modeling or data analysis? Give a specific example and the outcome.
• How do you identify and fix bugs in a complex energy model like the Energy Policy Simulator?
• How would you analyze the emissions impact of a specific energy or industrial policy proposal?
• How do you turn model output into a clear report, presentation, or graphic for external partners?
• How would you respond to a short-term research request from a philanthropic partner with a tight deadline?

Common mistakes to avoid

• Applying without showing any direct experience with the Energy Policy Simulator or a comparable energy model.
• Claiming AI agent proficiency without concrete examples of how you used agents in quantitative modeling or data analysis.
• Ignoring the hybrid and travel requirements, or not stating your location and ability to be in the San Francisco or Washington D.C. office.