Environmental Data Specialist

FLSA Status:

Exempt
Grade: 11

The Environmental Data Scientist supports interdisciplinary projects across water research, land stewardship, water
planning and policy, and sustainable communities. This position combines environmental science, quantitative analysis,
modeling, coding, and data management to help Oka’ staff and project partners better understand complex
environmental systems and support science-based decision-making.

The ideal candidate will have a strong foundation in environmental sciences and the technical ability to analyze, model,
and interpret environmental data using tools such as Python, R, SQL, GIS, and related analytical platforms. This position
will work across Oka’s focus areas to develop models, automate analytical processes, evaluate environmental datasets,
support research and field activities, and translate technical results into useful information for researchers,
communities, resource managers, and decision makers.

• Conduct quantitative analysis of environmental datasets related to water resources, watershed conditions, land
  management, climate, infrastructure, and other Oka’ research and community projects.
• Develop, apply, and evaluate environmental models to better understand environmental processes, assess
  conditions, evaluate scenarios, and support project management decisions.
• Develop scripts, analytical tools, and automated workflows using Python, R, SQL, or similar programming
  languages to process, analyze, model, and visualize environmental data.
• Assist project teams with the selection, application, interpretation, and refinement of environmental and
  statistical modeling approaches.
• Integrate environmental datasets from multiple sources, including field observations, laboratory results, GIS and
  remote sensing products, monitoring networks, databases, and publicly available datasets.
• Apply GIS and spatial analysis techniques to evaluate environmental conditions, identify spatial patterns,
  develop project tools, and support environmental modeling.
• Develop maps, figures, dashboards, reports, and other visualization products that clearly communicate
  environmental conditions, analytical findings, model outputs, and project results.
• Maintain organized and reproducible datasets, analytical workflows, code, model documentation, and metadata
  so that technical work can be understood and used by other project staff.
• Other duties as assigned by Supervisor or designee.

Omission of specific statement of duties does not exclude them from the position if the work is similar, related or a logical assignment to the position.

• Master’s degree in Environmental Science, Environmental Engineering, Water Resources, Hydrology, Natural
  Resources, Ecology, Geography, Geospatial Science, or a closely related field.
• Strong understanding of environmental science principles and the ability to interpret environmental and natural
  resource data within the context of physical and ecological systems.
• Experience analyzing environmental, water resource, natural resource, or related scientific datasets.
• Experience using GIS for spatial analysis, environmental assessment, mapping, or development of map-based
  project tools.
• Experience with quantitative analysis, statistical analysis, environmental modeling, or other computational
  approaches used to evaluate environmental systems.
• Experience working with structured datasets and applying appropriate data organization, documentation, and
  quality-control practices.
• Proficiency with Python, R, SQL, or similar programming languages for environmental analysis, modeling,
  scripting, and workflow automation.
• Experience developing, calibrating, validating, or applying environmental, hydrologic, watershed, ecological,
  statistical, or geospatial models.

• Ph.D. in Environmental Science, Environmental Engineering, Water Resources, Hydrology, Natural Resources,
  Ecology, Geography, Geospatial Science, or a closely related field.
• Familiarity with tools i.e. SQL, Python, or R for scripting and automation.
• Experience applying artificial intelligence or machine learning tools to environmental or scientific applications,
  including data analysis,…