Machine Learning Scientist

GoodPower•,

15h•$160,000 - $190,000•Remote

About The Position

GoodPower 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, GoodPower 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. GoodPower 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. GoodPower 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.

Requirements

PhD in machine learning, computer science, statistics, or a related quantitative field, with 3-5 years of applied ML experience. Exceptional recent PhD graduates with substantial applied work will also be considered.
Evidence of turning ambiguous problems into rigorous experiments and useful AI/ ML capabilities, including appropriate baselines, validation, uncertainty assessment, and error analysis.
Hands-on experience with NLP and LLM applications, including embeddings, retrieval-augmented generation, and systematic evaluation.
Strong Python skills and experience with tools such as PyTorch/ TensorFlow, scikit-learn, and pandas; proficiency in SQL, version control, and reproducible research. Experience deploying and evaluating models in real workflows, independently or with engineering partners.
Sound statistical judgment and the ability to work with heterogeneous, incomplete, or noisy data. Ability to distinguish predictive associations from evidence of causation.
Ability to own technical work, prioritize under uncertainty, and communicate findings to technical and nontechnical colleagues. Curiosity about energy and climate challenges and motivation to learn from domain experts.

Nice To Haves

Experience with energy, climate, grid, infrastructure siting, permitting, regulatory, or public participation data; geospatial analysis, graph ML, or time-series forecasting.
Publications, open-source contributions, or deployed projects demonstrating methodological depth, originality, and practical impact.

Responsibilities

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.
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 false negatives. Develop approaches for limited or noisy labels, and define when systems should defer to human judgment.
Make research reproducible through versioned data, code, and experiments. Document assumptions, limitations, and failure modes; monitor drift and use feedback to guide model improvement.
Build retrieval and synthesis systems that turn heterogeneous documents and data into verifiable answers and actionable intelligence. Improve retrieval, ranking, source quality, citation accuracy, and faithfulness through systematic evaluation.
Develop agents that use tools, models, and internal services to complete useful workflows. Define permissions, failure recovery, and human review for consequential actions, and evaluate complete task outcomes.
Evaluate hosted and open-weight models, prompting, fine-tuning, and distillation against quality, privacy, latency, and cost requirements. Build reusable capabilities where they offer an advantage across use cases.
Establish evaluation and monitoring for LLM and agent systems, including factual accuracy, robustness, task completion, and operating cost. Use controlled experiments to verify improvements before release.
Shape reusable data representations, feature pipelines, retrieval services, and model interfaces. Introduce ontologies or knowledge graphs where relationships and structured reasoning improve performance or usability.
Partner with software and ML engineers to move validated prototypes into reliable products. Define data and API contracts, deployment requirements, monitoring, and retraining plans while balancing performance, maintainability, and cost.
Identify high-value opportunities beyond existing platform roadmaps. Propose new capabilities and research directions, define milestones and decision points, and help prioritize the AI/ML portfolio.
Work with product, research, communications, and program teams to understand user decisions and constraints. Explain results clearly, challenge unsupported assumptions, and help colleagues apply AI/ML effectively.
Define and track outcomes such as decision quality, predictive performance, user adoption, analyst time saved, and cost per workflow. Use evidence to scale successful approaches, revise weak ones, and stop work that does not deliver value.

Benefits

Medical, dental, and vision insurance with employee and dependent premiums paid at 95-100% by GoodPower.
2-4 weeks of paid vacation annually, depending on seniority
13 paid holidays and 2 floating holidays
2 weeks of US office closure
10 days of paid sick leave annually.
403(b) with 3% employer contribution/match.
Charitable giving: 1:1 match for charitable contributions ($250 per year), plus 2 paid days off for volunteering.
Education reimbursement up to $1,500 per year.
Healthcare Flexible Spending Account (FSA) and Health Savings options
Stipend of $200/month (for cell phone, wellness expenses, etc.)
8 weeks of paid sabbatical after 5 years of employment
8 weeks of paid parental leave