Air Quality Data Analyst

With world attention on both the environment and the economy, Environmental Defense Fund (EDF) is where policymakers and business leaders turn for win-win solutions. By focusing on strong science, uncommon partnerships and market-based approaches, we tackle urgent threats with practical solutions. We are one of the world’s largest environmental organizations, with more than two million members and a staff of approximately 630 scientists, economists, policy experts, and other professionals around the world. We operate in 22 geographies with unique projects running across four programs. You will be part of a vibrant workplace that welcomes diverse perspectives, talents and contributions, where innovation and results are a way of life.

Location can be Austin, TX (preferred) or San Francisco, CA

The Office of the Chief Scientist is the nexus of science at EDF, ensuring that all EDF positions are based on the best available natural and social science. To be most effective in this role, the Office of the Chief Scientist provides programmatic support for all scientists at EDF and fosters relationships with scientists outside the organization.


The Air Quality Data Analyst will be a key team member in the Office of the Chief Scientist and the cross-cutting Clean Air Innovation project, where our interdisciplinary team is working to demonstrate and scale hyperlocal air quality assessments from low and medium cost air quality sensors. The Air Quality Data Analyst will contribute to the team’s goal of accelerating the adoption and impact of air pollution mitigation strategies in diverse geographies by supporting a team of air quality scientists who are refining hyperlocal data collection, analysis and presentation approaches.



  • Assess performance of low cost sensor data by comparison with reference instrument data.
  • Clean air quality data sets from mobile deployments of various grades of instrumentation and stationary deployments of low cost sensors.
  • Run existing algorithms for a variety of air quality data sets.
  • Parse, format, and aggregate data in preparation for various analyses.
  • Set up scheduling of certain data processes for regularly timed execution in the cloud.
  • Create static and interactive maps of results from geospatial analyses.
  • Develop and apply systematic approaches to characterize temporal and spatial patterns in air quality data.
  • Help to develop and test algorithms to derive emission ratios, correlation coefficients/spatial regressions for air quality data sets.
  • Evaluate attributes of traffic activity datasets and conduct integrative analyses with air quality datasets.


  • Strong spatial analysis and mapping skills using  e.g. ArcGIS, QGIS, or GRASS GIS
  • Experience in one or more of the following:
    • Statistical software (e.g. “R”)
    • SQL databases (e.g. PostgreSQL, Google BigQuery, Microsoft SQL Server or Access), and advanced SQL scripting capability
    • JavaScript programming
    • Comfort and familiarity with accessing and manipulating spatial data sets using Python scripting (e.g. arcpy or open source equivalent)
  • Demonstrated ability to conduct independent scientific data analyses and experience with interpreting scientific results
  • Experience in data visualization programming (e.g. Tableau, Plotly, Leaflet) preferred
  • Strong interpersonal and written communication skills
  • Excellent attention to detail and record keeping abilities
  • Bachelor’s degree in a quantitative field (e.g., mathematics, statistics, engineering, physics, computer science), with at least 4 years of work experience or Master’s degree in an environmentally relevant science field; background in atmospheric and/or environmental sciences a plus

Environmental Defense Fund is an equal opportunity employer where an applicant's qualifications are considered without regard to race, color, religion, sex, national origin, age, disability, veteran status, genetic information, sexual orientation, gender identity or expression, or any other basis prohibited by law.