OPS Environmental Data Analyst

The OPS Environmental Data Analyst at Florida Gulf Coast University supports environmental research by managing and analyzing datasets using programming languages and GIS tools. Key responsibilities include statistical analysis, data visualization, GIS tasks, and collaboration with teams. A Bachelor's degree in a relevant field and proficiency in R and/or Python are required. The position necessitates residing in Southwest Florida for periodic in-person collaboration.
Job Summary
The OPS Environmental Data Analyst will support environmental research and project teams by managing, analyzing, and visualizing environmental datasets using programming languages and GIS tools. This position requires a technically proficient individual with experience in scientific research, statistical analysis, and data communication. The role emphasizes collaboration, attention to detail, and adaptability in a fast-paced, multidisciplinary environment.

Job Description
Typical Duties Include but Are Not Limited To:
• Analyzes environmental datasets using Python, R, and SAS, applying statistical techniques to identify patterns, trends, and anomalies.
• Creates high-quality data visualizations using programming and graphical user interface (GUI) tools.
• Support GIS-related tasks, including spatial analysis, map creation, and integration of geospatial data into broader datasets.
• Contributes to the development of automated data processing scripts and reproducible workflows.
• Organizes, archives, and documents large datasets, including QA/QC protocols and metadata standards.
• Supports research teams in preparing data-driven content for reports, presentations, and publications.
• Coordinates with staff and external partners to ensure consistent and accurate data handling.
• Participates in technical trainings and workshops to stay current on tools and methods relevant to environmental data science.
Other Duties:
• Maintains documentation of coding workflows using appropriate tools and platforms.
• Provides support for statistical modeling, spatial interpolation, or time-series analysis.
• Drafts standard operating procedures for data management and analysis.
• Support outreach and education efforts with data storytelling products and clear visual communication materials.

Additional Job Description
Required Qualifications:
• Bachelor’s degree in Environmental Science, Geography, Data Science, Statistics, Computer Science, or a related field.
• Demonstrated experience with R and/or Python for scientific or environmental data analysis.
• Experience using GIS software such as ArcGIS Pro or QGIS.
• Knowledge of statistics and experience conducting or supporting scientific research.
• Must reside in the Southwest Florida region and be available for periodic in-person collaboration.
Preferred Qualifications:
• Graduate-level coursework or research experience in environmental science or data analytics.
• Familiarity with environmental data types (e.g., water quality, hydrology, climate, biological metrics).
• Proficiency in data visualization tools and libraries.
• Experience with version control, relational databases, or cloud-based data platforms.
• Experience using SAS for data analysis, including SAS coding and SAS Viya tools for advanced analytics and visualization.
• Understanding of reproducible research practices and open data standards.
Knowledge, Skills & Abilities

All employees are expected to:
• Communicate effectively, both verbally and in writing, with colleagues, partners, and external stakeholders.
• Work independently and collaboratively to manage deadlines and contribute to project goals.

Position-Specific Knowledge, Skills & Abilities:
• Technical proficiency in coding, data cleaning, statistical analysis, and visualization using programming tools.
• Ability to interpret scientific and spatial data and translate it into meaningful graphics and insights.
• Experience managing complex datasets with attention to version control and metadata integrity.
• Strong organizational and time management skills to support multiple ongoing projects.
• Willingness to learn new tools, attend technical trainings, and apply evolving best practices in data science.