This engineer will build data systems and machine learning tools that help policy and energy teams assess climate interventions, compare local contexts, and identify renewable energy opportunities. The work spans policy data, community input, predictive models, and the infrastructure needed to put those tools into use.
The focus is on making climate policy knowledge usable across places: connecting public records and expert insights so communities and decision-makers can learn from interventions elsewhere. The analysis also supports renewable energy planning and advocacy, linking technical research with practical climate action at municipal and federal levels.
This suits a mid-career data scientist or ML engineer who can build with unstructured data, explain analytical findings to non-technical partners, and is motivated to apply rigorous modeling to climate policy and energy transition questions.
Job FAQs
What are the main missions and responsibilities of this role?
The role combines ML engineering, data infrastructure, and policy analysis. It involves turning government records, policy documents, and community input into structured knowledge systems, then developing models that assess policy effectiveness, transferability, and energy development potential.
The longer-term aim is to help policy specialists and communities compare climate interventions across locations, understand how implementation unfolds, and use evidence to inform planning and advocacy.
Key learning opportunities for this job
The position could deepen experience in LLM and NLP applications, knowledge graphs, model evaluation, and cloud-based MLOps. Working with policy documents, municipal data, and energy records may also build practical knowledge of local governance, federal land management, and renewable energy planning.
Close collaboration with policy experts and community stakeholders could develop the ability to communicate technical findings for applied decisions. The posting does not specify formal training, mentorship, or certification programs.
How does the ideal candidate look like (experience, skills)?
The posting seeks a data scientist or ML engineer with 3-5 years of experience, strong Python and SQL skills, cloud experience, and hands-on work building LLM applications or structured knowledge systems. Experience handling varied unstructured data and creating reliable production pipelines is relevant.
A good fit would also bring sound modeling and evaluation judgment, clear communication, and comfort working with policy specialists on ambiguous questions. Climate policy, civic data, municipal governance, or energy transition experience is presented as useful, rather than mandatory.
Advice to stand out and make a successful application
Use the application and take-home assessment to show a complete, well-reasoned workflow: how you would extract policy information from source documents, preserve provenance, validate results with experts, and assess model quality. Explain trade-offs and error handling, not only model performance.
Connect a concrete technical example to the role's public-interest context, such as comparing interventions across jurisdictions or analyzing community sentiment. Be prepared to discuss how you would communicate uncertainty to policy experts and protect sensitive municipal data.
What aspects of the company's sustainability is this role likely to focus on?
The role's sustainability focus is climate policy and energy transition, with particular attention to identifying interventions that may work across different communities and to assessing renewable energy development opportunities. Its data systems could help connect policy evidence with local conditions.
The organization also describes community action and advocacy as part of its work. Analysis of sentiment and campaign interactions may inform those efforts, though the role's primary emphasis is technical research and decision support.
What are the main challenges someone in this role might face?
A likely challenge is assembling reliable, comparable evidence from inconsistent policy documents, municipal records, and government datasets. Linking extracted information to its source and distinguishing model findings from uncertainty will matter when results may inform public decisions.
The role also bridges technical work with policy and community contexts. Models must be useful across different local conditions, while data governance must account for sensitive information and changing data quality.
How could a typical day look like for someone in this position?
A typical day could involve reviewing data from a government or municipal source, improving an extraction pipeline, and checking whether policy elements have been correctly linked to their source documents. The engineer may also train or evaluate models and review findings with policy colleagues.
The balance is likely to shift between coding, data quality and infrastructure work, analysis of energy or community data, and explaining results to non-technical partners. Some days may focus on deployment and monitoring, while others center on research questions or expert validation.
What are the opportunities for professional growth and development in this role?
The work could build a broad profile across applied ML, LLM systems, cloud infrastructure, and climate policy analysis. Ownership of models and data pipelines may also provide experience in production evaluation, governance, and translating technical evidence into planning tools.
Potential next steps could include deeper specialization in climate analytics or technical leadership of data products and research systems. The posting does not specify a formal promotion path, so growth would likely depend on project scope and organizational needs.
The main stakeholders you might be interacting with
Likely partners include internal policy researchers and advocates who can validate extracted policy information and interpret findings. The role also calls for collaboration with community stakeholders and federal planners, particularly when translating analysis into usable planning insights.
Technical work may involve colleagues responsible for data sources, APIs, cloud infrastructure, and advocacy analysis. Government agencies and municipal records are important information sources, although direct engagement with their custodians is not specified.
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