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 organization supports philanthropic grantmaking strategy through quantitative modeling and analysis. This role sits at the intersection of policy research and grantmaking, which is unusual for a modeling position.

About the role

You will modify, run, and maintain the Energy Policy Simulator (EPS) and other energy and climate models to design policy scenarios, calibrate outputs, and identify bugs. You will also conduct advanced data analysis on large datasets, build quantitative models using AI agents, and turn findings into reports, presentations, and graphics for partners. Your work directly informs how philanthropic dollars are allocated to climate and energy policy efforts.

A typical day

A typical day might involve running policy scenarios in the Energy Policy Simulator, debugging model outputs, and analyzing large datasets with AI agents or Excel. You could also draft a partner-facing report or presentation and respond to a short-term research request on energy legislation. The role mixes independent quantitative work with synthesis for external partners, and office days in San Francisco or Washington D.C. add in-person collaboration.

Who Energy Innovation is looking for

• 4–6 years of experience in climate, energy, or environmental analysis, with hands-on quantitative modeling work.
• Advanced proficiency with Excel, AI agents, and data visualization tools; comfortable working with large datasets.
• Solid understanding of energy systems, policy design, and statistical methods, including emissions impact analysis.
• Able to translate model outputs into clear reports and presentations for non-technical partners, and willing to travel domestically at least twice yearly.

Tips for this application

• Show direct experience with the Energy Policy Simulator (EPS) or comparable energy/climate models in your resume or cover letter. Name the models you have modified, run, or maintained.
• Quantify your data work. State the size of datasets you handled, the tools you used (Excel, AI agents, visualization software), and the policy questions your analysis answered.
• Address the hybrid requirement upfront. Confirm you can work one to three office days per week in San Francisco or Washington D.C., or clarify your remote setup if the posting allows remote.
• Connect your modeling work to grantmaking or philanthropic strategy if you have that background. If not, explain how your analysis has informed policy or funding decisions.
• Include a work sample or description of a report, presentation, or graphic you produced from quantitative analysis. This role requires partner-facing outputs, not just modeling.

What to cover in your cover letter

Your experience modifying, running, or maintaining the Energy Policy Simulator or similar energy/climate models. A specific example of designing policy scenarios, calibrating outputs, and identifying bugs or improvements. How you have used AI agents and data visualization tools to build quantitative models or analyze large datasets. Your ability to synthesize findings into reports, presentations, and graphics for external partners, and your interest in supporting philanthropic grantmaking strategy.

Research before applying

• Read the Energy Policy Simulator documentation and any public model versions to understand its structure, inputs, and outputs.
• Review Energy Innovation's published reports and policy analyses to see how modeling informs their grantmaking recommendations.
• Look into the philanthropic grantmaking context: who funds energy and climate policy work, and how quantitative analysis shapes funding decisions.
• Check recent energy legislation and regulations at the federal and state level, especially those related to emissions impacts, to prepare for research requests.

Likely interview topics

Based on the job description, expect questions about:

• Walk through a time you modified or maintained an energy or climate model. What was the policy question, and how did you calibrate outputs?
• How have you used AI agents in quantitative modeling or data analysis? Describe the tools and the results.
• Explain how you would design a policy scenario to estimate emissions impacts. What data would you need, and what assumptions would you check?
• Describe a report, presentation, or graphic you created from model outputs for a non-technical audience. How did you decide what to include?
• How would you handle a short-term research request from an external partner on energy legislation or regulations? Give a concrete example or approach.

Common mistakes to avoid

• Submitting a generic cover letter that does not mention the Energy Policy Simulator or energy/climate modeling specifically.
• Listing data analysis skills without showing how you applied them to policy design, emissions impacts, or statistical methods.
• Ignoring the hybrid and travel requirements, or failing to confirm you can meet them in San Francisco or Washington D.C.