Environmental Data Scientist: Geospatial & Climate

Lynker Technologies; Boulder (CO); On-site; USD 90,000 - 140,000; Full time

Benefits offered by this job

Personalized career growth plans

Job summary

Lynker Technologies is seeking an Environmental Data Scientist to join our Boulder team. You will organize and structure environmental datasets, define rules, and design data schemas to support transparent, repeatable analyses.

The role requires experience with R/Python, GIS tools, and environmental data modeling. Collaboration across science, engineering, and platform teams is essential to deliver robust data products.

Qualifications

• Master's in environmental science or related field, or Bachelor's with 2 years environmental data work.
• Strong environmental and data science background.
• Proficiency in R or Python for data wrangling, analysis, and modeling.
• Experience with GIS tools and spatial datasets (ArcGIS, QGIS, Google Earth Engine).
• Ability to translate domain logic into clear, testable rules.
• Strong scientific writing skills.

Responsibilities

• Data organization: organize, catalog, and structure heterogeneous environmental and geospatial datasets with metadata and provenance.
• Analytical logic: define how rules are represented, versioned, and evaluated with traceable lineage.
• Data product design: define inputs/outputs, schemas, and how schemas evolve.
• Domain reasoning: apply environmental/hydrologic knowledge to weighting and encoding rules.
• Validation & QA: develop checks and validation logic for explainable results.
• Documentation & collaboration: document schemas and sources; work with Science, Engineering, and Platform teams.

Skills

R

Python

Geospatial data

Technical writing

Education

Master's in environmental science or related field

Bachelor's + 2 years relevant experience

Tools

ArcGIS

QGIS

Google Earth Engine

Job description

Lynker Technologies is seeking an Environmental Data Scientist to join our Boulder team. You will organize and structure environmental datasets, define rules, and design data schemas to support transparent, repeatable analyses.

The role requires experience with R/Python, GIS tools, and environmental data modeling. Collaboration across science, engineering, and platform teams is essential to deliver robust data products.