Machine Learning Engineer / Data Scientist - Climate and Energy Policy

Planet Reimagined is a mission-driven organization advancing climate solutions through policy, civic action, and renewable energy initiatives. They are seeking a Machine Learning Engineer / Data Scientist to build knowledge systems, machine learning models, data infrastructure, and analytical tools for climate policy analysis, renewable energy optimization, and scalable climate interventions.

Responsibilities:

• Build LLM-powered knowledge graphs, wikis, and other structured datasets from policy documents, municipal records, community sentiment, and federal energy data, along with the human-readable layers rendered from them
• Develop LLM and NLP pipelines that extract policy elements, resolve entities, and link them across sources
• Track data provenance so every policy element traces back to its source document, passage, and extraction method
• Design human-in-the-loop workflows where policy experts validate extracted policy elements, and their corrections improve the models, scripts, and prompts
• Construct policy timelines by extracting and ordering events to show how climate solutions unfold over time
• Design, train, and deploy models for policy effectiveness scoring, solution replicability, and energy development potential
• Build recommendation and graph-based models that match proven climate policies to a specific city, county, or community
• Own the full modeling lifecycle: baselines, feature engineering, model selection, tuning, and error analysis
• Develop evaluation frameworks to measure model, extraction, and LLM output quality against expert-validated ground truth
• Analyse community-stentiment data to quantify support for climate policies within surveyed localities
• Build systems to facilitate rigorous comparative analysis, helping policy experts map when, where, and how policies succeed and identify causal dynamics that may transfer to other contexts
• Analyze policy timelines to measure adoption lags, implementation speed, and time to impact
• Perform exploratory analysis and predictive modeling to find patterns in climate solution adoption and renewable energy development opportunities
• Communicate findings through visualizations and automated reporting for climate and energy transition planning
• Deliver insights from the analysis of interaction and call-to-action conversion data to improve in-concert advocacy efforts
• Build reproducible training, evaluation, and deployment pipelines with experiment tracking, versioning, and a model registry
• Automate CI/CD for models, with validation gates and staged rollouts
• Maintain the data pipelines and API integrations (BigQuery, Cloud Functions) that feed models from government, community, and federal data sources
• Monitor model performance, data drift, and knowledge graph quality, with data governance for sensitive municipal data and PII

Requirements:

• 3-5 years of experience in data science and ML engineering, preferably in GCP/AWS/Azure ecosystem
• Strong proficiency in LLM application design, context engineering strategies, Python, and SQL
• Hands-on experience building functional knowledge systems for LLM applications
• Proven ability to work with varied, unstructured data sources including municipal records and reports, news coverage, video documentation, and government datasets
• Strong problem-solving skills and ability to thrive in ambiguous climate policy and energy environments
• Excellent communication skills for cross-functional collaboration with policy experts, community stakeholders, and federal planners including turning data analysis into data storytelling outputs
• Background or strong interest in climate policy, municipal governance, federal land management, or energy transition planning
• Experience with government data sources, regulatory documents, federal climate records, and civic data standards
• Knowledge of policy analysis frameworks, community engagement measurement, and renewable energy development processes
• Previous work in mission-driven organizations focused on local government, federal policy, or climate action