Data Scientist

SWCA Environmental Consultants is seeking a Data Scientist to join our Technology and Innovation team in a fully remote capacity from anywhere within the United States. In this role, you will support advanced analytics, machine learning, and data science initiatives across SWCA's business lines and environmental consulting projects. You will collaborate with technology teams, environmental scientists, project managers, and other multidisciplinary stakeholders to develop, deploy, and maintain production-ready applications and analytical solutions that support both internal operations and client-facing work. The ideal candidate is a strategic thinker with strong technical expertise, a passion for applying data science within the environmental sector, and the ability to thrive in a collaborative, evolving environment. You will independently deliver defined AI and data projects from stakeholder discovery through deployment and adoption, drawing on team expertise for complex design decisions.

This is a full-time, regular, salaried/exempt, benefits-eligible, and distributed (=remote) opportunity that may be based anywhere within the United States.

Application Deadline: Applications are reviewed on a rolling basis, and interviews may be scheduled as qualified candidates are identified. This position may close without prior notice once a strong candidate pool is established.

• Scope projects with stakeholders, define success measures, and select appropriate statistical, machine learning, or AI approaches for business and environmental problems.
• Design, deploy, and maintain Python services for internal AI applications, including retrieval-augmented generation (RAG), structured outputs, tool calling, and API integrations.
• Build AI agents with Copilot Studio and custom frameworks; manage internal and stakeholder review, release, and ongoing improvement.
• Develop document-intelligence pipelines using content extraction, optical character recognition (OCR), embeddings, and vector search; preserve source traceability and expert review.
• Build and maintain SQL and cloud data pipelines for analytics, models, and agents; validate data quality and integrate enterprise data sources.
• Develop evaluation datasets and repeatable tests; compare models and retrieval methods for quality, accuracy, cost, and response time, and monitor performance after deployment.
• Deliver maintainable software through GitHub, automated testing, Docker, and deployment workflows; validate work produced with approved AI coding assistants.
• Apply access controls, logging, auditability, and human oversight with technology and governance partners; contribute to reusable service, evaluation, and agent standards.
• Drive adoption through documentation, training, and user support; measure outcomes such as time saved, decision quality, usage, and client value.
• Mentor associate staff, review technical work, and help the team make informed model, platform, and deployment decisions.

• Bachelor's degree in Computer Science, Data Science, Statistics, Geography, Environmental Science, or a related field.
• Three (3) years of experience in a Data Science, Data Analytics, GIS Analytics, or related technology position.
• Experience delivering large language model (LLM) applications to real users, such as RAG systems, agents with tool calling, or document AI, including evaluation and iteration beyond a prototype.
• Proficiency in Python services built with FastAPI or similar frameworks; experience with Git code review, automated tests, Docker, and continuous integration and deployment (CI/CD).
• Cloud deployment experience and data engineering skills in SQL and pipeline development.
• Ability to evaluate AI quality, cost, and response time; implement secure data access, traceable outputs, monitoring, and appropriate human review.
• Ability to scope ambiguous needs with nontechnical stakeholders, deliver independently, and support adoption; clear communication for consulting and client-facing work.

Preferred Qualifications:

• Master's degree in a related field.
• Five (5) years of experience in a Data Science, Data Analytics, GIS Analytics, or related technology position.
• Experience applying statistical and machine learning methods, validating models, assessing data quality, and selecting approaches appropriate to the problem.
• Experience with Azure OpenAI, Azure Machine Learning, Container Apps, Microsoft Fabric, or Azure Data Factory.
• Experience with Copilot Studio, Power Platform, Microsoft Graph, SharePoint, or Model Context Protocol (MCP) integrations.
• Experience with OCR pipelines, self-hosted inference such as vLLM, GPU workflows, or tools for repeatable LLM evaluation and operational monitoring.
• Experience in environmental consulting or other auditable applications of AI; experience mentoring colleagues and supporting responsible AI adoption.

• Scope projects with stakeholders, define success measures, and select appropriate statistical, machine learning, or AI approaches for business and environmental problems.
• Design, deploy, and maintain Python services for internal AI applications, including retrieval-augmented generation (RAG), structured outputs, tool calling, and API integrations.
• Build AI agents with Copilot Studio and custom frameworks; manage internal and stakeholder review, release, and ongoing improvement.
• Develop document-intelligence pipelines using content extraction, optical character recognition (OCR), embeddings, and vector search; preserve source traceability and expert review.
• Build and maintain SQL and cloud data pipelines for analytics, models, and agents; validate data quality and integrate enterprise data sources.
• Develop evaluation datasets and repeatable tests; compare models and retrieval methods for quality, accuracy, cost, and response time, and monitor performance after deployment.
• Deliver maintainable software through GitHub, automated testing, Docker, and deployment workflows; validate work produced with approved AI coding assistants.
• Apply access controls, logging, auditability, and human oversight with technology and governance partners; contribute to reusable service, evaluation, and agent standards.
• Drive adoption through documentation, training, and user support; measure outcomes such as time saved, decision quality, usage, and client value.
• Mentor associate staff, review technical work, and help the team make informed model, platform, and deployment decisions.

• Bachelor's degree in Computer Science, Data Science, Statistics, Geography, Environmental Science, or a related field.
• Three (3) years of experience in a Data Science, Data Analytics, GIS Analytics, or related technology position.
• Experience delivering large language model (LLM) applications to real users, such as RAG systems, agents with tool calling, or document AI, including evaluation and iteration beyond a prototype.
• Proficiency in Python services built with FastAPI or similar frameworks; experience with Git code review, automated tests, Docker, and continuous integration and deployment (CI/CD).
• Cloud deployment experience and data engineering skills in SQL and pipeline development.
• Ability to evaluate AI quality, cost, and response time; implement secure data access, traceable outputs, monitoring, and appropriate human review.
• Ability to scope ambiguous needs with nontechnical stakeholders, deliver independently, and support adoption; clear communication for consulting and client-facing work.

Preferred Qualifications:

• Master's degree in a related field.
• Five (5) years of experience in a Data Science, Data Analytics, GIS Analytics, or related technology position.
• Experience applying statistical and machine learning methods, validating models, assessing data quality, and selecting approaches appropriate to the problem.
• Experience with Azure OpenAI, Azure Machine Learning, Container Apps, Microsoft Fabric, or Azure Data Factory.
• Experience with Copilot Studio, Power Platform, Microsoft Graph, SharePoint, or Model Context Protocol (MCP) integrations.
• Experience with OCR pipelines, self-hosted inference such as vLLM, GPU workflows, or tools for repeatable LLM evaluation and operational monitoring.
• Experience in environmental consulting or other auditable applications of AI; experience mentoring colleagues and supporting responsible AI adoption.