About Resources for the Future

Resources for the Future (RFF) is an independent, non-profit research institution dedicated to improving environmental, energy, and natural resource decisions through impartial, evidence-based economic research and policy engagement. For nearly 75 years, RFF has shaped environmental policy by providing trusted research insights and practical solutions that address climate change, energy transitions, and natural resource challenges. Through innovative modeling and analysis, the organization works to promote a healthy environment and a thriving economy.

The Role

RFF is seeking a Research Analyst or Senior Research Analyst to join its Research and Policy Engagement team in Washington, DC. This hybrid role offers the opportunity to contribute to influential research projects examining the impacts of environmental and energy policies. The successful candidate will support one or more advanced economic models, including the Engineering, Economic, and Environmental Electricity Simulation Tool (E4ST) and the Dynamic Regional General Equilibrium Model (DR-GEM), helping to generate evidence that informs critical policy decisions.

Key Responsibilities

Research and Model Development

• Contribute to the development and maintenance of power sector and/or general equilibrium models.
• Support research activities through literature reviews, data management, and analytical tasks.
• Analyze and visualize large datasets to inform research outputs.
• Collaborate on reports, academic papers, and funding proposals.

Technical Analysis and Data Management

• Assemble, organize, and interpret complex datasets.
• Apply programming skills using tools such as Python, R, or Julia.
• Support project teams in delivering high-quality, evidence-based research.
• Undertake additional analytical duties as required.

Collaboration and Project Delivery

• Work closely with RFF Fellows and multidisciplinary teams.
• Manage competing priorities and meet project deadlines.
• Adapt to evolving research needs and contribute proactively to team objectives.

Requirements

• Bachelor’s degree in economics, engineering, mathematics, computer science, operations research, natural sciences, or a related field.
• For Senior Research Analyst roles, either a master’s degree in a relevant field or a bachelor’s degree with at least two years of relevant experience.
• Experience or coursework using programming languages such as Python, R, or Julia.
• Interest in climate, energy, environmental, or natural resource policy.
• Experience managing and analyzing datasets.
• Strong organizational, analytical, and communication skills.
• Ability to work collaboratively and independently.

Desirable qualifications include experience with GIS, GAMS, optimization techniques, Git, energy system databases, power sector modeling, general equilibrium modeling, and data science methodologies.

What They Offer

• Salary of $56,500–$69,500.
• Comprehensive medical, dental, and vision coverage.
• 35-hour work week.
• Flexible summer schedule, including four-day work weeks from June to September.
• Hybrid working arrangements with three office days per week.
• Eleven paid holidays annually.
• Office closure between Christmas and New Year.
• Four weeks of paid time off from the start, increasing to five weeks after five years of service.
• Fifteen paid sick days each year.
• 403(b) retirement contributions equal to 5% of salary.
• On-site fitness facility with free classes.
• Parking and commuter benefits.

How to Apply

Interested candidates should apply through RFF’s online application system. Applications should include a resume outlining relevant modeling experience, a cover letter describing interest in the role, an original sole-authored writing sample of no more than 20 pages, academic transcripts (unofficial transcripts accepted), and a coding sample. Applications will be reviewed on a rolling basis until the position is filled. Candidates may use AI tools for coding samples but should be prepared to explain how AI was used. Writing samples must be entirely their own work.