Global Water Security Center

Providing decision makers with the most reliable, ground-breaking research, applied scientific techniques, and best practices so that the hydrologic cycle and its potential impacts can be put in a context for appropriate action and response by the United States

Environmental Data Scientist

The Global Water Security Center (GWSC) seeks both a postdoctoral fellow and an earlier career data scientist passionate about addressing global environmental challenges in the real world. We’re looking for candidates knowledgeable about environmental data and experience working with large datasets and conducting both spatial and temporal analysis. The candidates will work closely with Senior Data Scientists as well as with researchers across a range of disciplines to analyze water, weather, climate, and environmental data to solve water security issues.

The Postdoctoral Fellow must have a PhD or anticipate receiving one within 3 months. This is a one year position renewable for up to three years.

GWSC anticipates that the Data Scientist I candidate will have either a Master’s degree or a Bachelor’s degree coupled with more extensive work experience.

GWSC is a research-to-operations center, so the candidates will support the production of environmental information briefs in support of our stakeholders. The candidates will also be involved in a range of activities from exploratory analysis to development and implementation of data workflow to visualization and communication. The postdoctoral fellow in particular will have flexibility to select among a large number of data-driven questions that must be addressed to improve GWSC’s work. Applicants should be self-starters, analytically creative, willing to brainstorm, and inquisitive.

Continuation in this position is contingent upon availability of external funding derived from research programs or specific projects. To best execute its mission, the Office for Research & Economic Development (ORED) prefers that all ORED employees work on the campus of The University of Alabama. Postdoctoral Scholars and early career scientists are required to work on campus, though hybrid work agreements may be allowed after 6 months in the position. 

Skills and Knowledge
  • Experience with and understanding of environmental data
  • Experience managing and analyzing large, multi-dimensional datasets
  • Experience with statistical analyses. For example, experience with non-normally distributed data and statistical analyses including regressions, t-tests, nonparametric tests, power analyses, multivariate and/or ordination techniques
  • Demonstrable knowledge in a programming language or languages often applied to environmental datasets; examples could include R, Python, mySQL, MATLAB and JavaScript, or other GIScience.
About GWSC
 
GWSC is an applied research and operational center commissioned to respond to our nation’s need for water and environmental security insights. GWSC will inform national security partners and others with global interests in water and environmental security.

Our partners include the Department of Defense (DoD), other US Government agencies, private companies, and data producers from federal agencies and academia.
 
Please apply via the University of Alabama jobs site to the Global Water Security Center Postdoctoral Fellow or Data Scientist I position (linked below). In your cover letter, please note how you think your data analysis experience links to water security.
Salary Range

Commensurate with experience.

GWSC anticipates a midpoint salary for both positions of $70,000

Minimum Qualifications

See UA Careers Website 

Preferred Qualifications
  • Degree in any type of environmental science, economics, geography, psychology or related field working with large data sets
  • Experience with Bayesian statistics
  • U.S. citizen capable of receiving a security clearance

Postdoc additional  qualifications:

  • Experience developing scientific data-management tools/processes to manage large volumes of data originating from sensors or other continuous data sources.
  • Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real–world advantages and drawbacks.