Computational Social Scientist

Location: Northern

Remote-based in the US

Overview

Good Power is a global organization working to unlock the economic potential of decarbonization through research, innovation, strategic communications, and disruptive technology. We focus on advancing more affordable and reliable energy, expanding economic opportunity, strengthening energy security, supporting healthier and more resilient communities, and accelerating the transition to a predictable, secure, modern low-carbon economy. Operating at the intersection of data, digital media, emerging technology, behavioral research, and public engagement, Good Power develops and deploys scalable strategies designed to accelerate the adoption of decarbonized technologies and infrastructure—including renewable energy, regenerative agriculture, energy storage, and electric transportation.

Our work combines rigorous research and experimentation with advanced communications and innovation capabilities to better understand public attitudes, test effective interventions, and help build the social, economic, and market conditions necessary for large-scale transformation. Good Power is at a pivotal stage of organizational growth as we approach completion of the first year of our 2030 Strategic Plan. Over the next five years, we intend to significantly expand our reach, capabilities, and impact—investing deeply in innovation, AI-enabled systems, data and analytics, research and development, and next-generation communications infrastructure to help accelerate the global energy transition and the economic opportunities it creates.

Job Summary

Good Power is an evidence-based, nimble, and adaptive organization that routinely evaluates our programs to measure impact. Over the last decade, we have continually tested and refined our approach, and have successfully built a set of program offerings that blend cost-efficiency, rigor, scale, and measurable impact. Our programs have demonstrated success through external evaluation, including with partners at Stanford, Yale, and George Mason Universities.

We also collaborate regularly with research partners such as Analyst Institute, Grow Progress, One Minus Beta, and Tufts University’s Center for Information & Research on Civic Learning and Engagement (CIRCLE). The Computational Social Scientist, Applied AI will join Good Power’s Good Data Lab, using large-scale data, computational social science, machine learning, and artificial intelligence to answer important questions about energy, climate, politics, and behavior.

The role will develop new ways to understand how people think, communicate, behave, and respond to Good Power programs at a scale that would not be possible through traditional research methods alone. This role will identify and analyze large public and proprietary datasets and develop scalable approaches for analyzing text, behavioral, political, and other forms of social data. The work will combine large language models with established computational methods such as supervised classification, clustering, topic modeling, embeddings, and dictionary-based analysis, with an emphasis on rigorous validation, reproducibility, and selecting methods that fit the research question.

This position reports to the Chief Research Officer. Travel will be required to departmental meetings, staff retreats, and other events to represent the department and organization.

Key Responsibilities

• Data and methodological innovation:
  Identify novel public and proprietary datasets, data-linkage opportunities, and emerging computational approaches that expand Good Power’s ability to measure behavior, understand program impact, and study decarbonization at scale.
• Independent research:
  Develop and lead original research questions that advance Good Power’s understanding of decarbonization, public opinion, civic participation, and behavior change. Design rigorous studies using appropriate computational, statistical, and other methods, and exercise scientific judgment about study design, inference, limitations, and the conclusions supported by the evidence.
• Large-scale data analysis:
  Acquire, integrate, and analyze complex datasets to uncover patterns in…