Machine Learning Engineer – Data Scientist, Climate and Energy Policy

🔥 20 hours ago

🇺🇸 United States – Remote

💵 $120k - $150k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🚰 Data Engineer

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Planet Reimagined

WebsiteLinkedInAll Job Openings

11 - 50 employees

🤝 Non-profit

🌍 Social Impact

Non-profit • Social Impact

Planet Reimagined is an organization that incubates and scales creative solutions focused on impact, action, change, people, and policy. It practices action research and frames work around themes like a Neo-Industrial Revolution; program names visible in the available text include Common Grounds, Amplify, Grapevine, and Impact. The site structure references team, board, partners, financials, and donation/join options, indicating a mission-driven entity that supports projects, policy change, and community-focused initiatives.

📋 Description

• Build LLM-powered knowledge graphs, wikis, and structured datasets from policy documents, municipal records, community sentiment, and federal energy data • Develop LLM and NLP pipelines to extract policy elements, resolve entities, and link information across sources • Track data provenance from policy elements to source documents, passages, and extraction methods • Design human-in-the-loop workflows for policy-expert validation and model improvement • Construct policy timelines by extracting and ordering events • Design, train, and deploy models for policy effectiveness scoring, solution replicability, and energy development potential • Build recommendation and graph-based models matching proven climate policies to cities, counties, and communities • Own modeling lifecycle including baselines, feature engineering, model selection, tuning, and error analysis • Develop evaluation frameworks for models, extraction, and LLM output quality against expert-validated ground truth • Analyze community sentiment data to quantify support for climate policies • Build systems for comparative and causal analysis of policy success and transferability • Analyze policy timelines to measure adoption lags, implementation speed, and time to impact • Perform exploratory analysis and predictive modeling for climate solution adoption and renewable energy opportunities • Communicate findings through visualizations and automated reporting • Analyze 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 model registry • Automate model CI/CD with validation gates and staged rollouts • Maintain data pipelines and API integrations feeding models from government, community, and federal data sources • Monitor model performance, data drift, and knowledge graph quality, including governance for sensitive municipal data and PII

🎯 Requirements

• 3–5 years of experience in data science and ML engineering • Experience 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 • Experience 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 and data storytelling • Proficiency with BigQuery, GCP/AWS/Azure services, Pandas, NumPy, dbt, Apache Beam/Dataflow, REST APIs, Cloud Functions, Pub/Sub, Cloud Run, CI/CD with Cloud Build, Firestore, and vector databases • Background or strong interest in climate policy, municipal governance, federal land management, or energy transition planning is nice to have • Experience with government data sources, regulatory documents, federal climate records, and civic data standards is nice to have • Knowledge of policy analysis frameworks, community engagement measurement, and renewable energy development processes is nice to have • Ability to pass a background check • U.S. Citizenship or U.S. work authorization • Ability to provide 3 professional references including a recent supervisor

🏖️ Benefits

• Healthcare coverage • Dental coverage • Vision coverage • Retirement plans • Paid time off • Remote but highly collegial working environment

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