Machine Learning Scientist

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 seeks a Machine Learning Scientist to expand what our teams and software can accomplish through applied research, predictive modeling, and intelligent automation. You will identify where Artificial Intelligence/ Machine Learning (AI/ML) can improve decisions, unlock new offerings, and reduce the time and cost of delivering our work. Your mandate spans clean energy planning, public participation integrity, media and narrative intelligence, and organizational effectiveness.

You will own the scientific development of ML capabilities from problem definition and experimentation through validation and deployment with engineering partners. Success means demonstrably better decisions, more useful products, and greater team capacity, supported by rigorous evidence and clear communication of uncertainty. The Machine Learning Scientist will report to the Chief Innovation Officer.

Key Responsibilities Applied Research and Model Development

• Translate user needs and strategic challenges into tractable ML problems. Establish baselines, define success criteria, and prioritize experiments by potential impact, data readiness, and feasibility.
• Develop models for classification, entity resolution, anomaly detection, forecasting, ranking, and risk assessment using text, geospatial, energy, market, media, and organizational data. Combine sources to reveal patterns that improve decisions.
• Select and test methods suited to the problem, from statistical models and classical ML to deep learning, graph methods, and LLMs. Demonstrate when added complexity delivers meaningful value.
• Use predictive models, optimization, and scenario analysis to evaluate options and allocate resources. Apply experimental design and causal inference where the data and assumptions support credible conclusions about intervention effectiveness.

Data Quality and Scientific Evaluation

• Build representative datasets, labeling strategies, and evaluation benchmarks. Prevent leakage and test performance across time, geography, groups, and unfamiliar contexts using validation appropriate to the intended use.
• Quantify uncertainty, calibration, and the consequences of false positives and…