Intermediate Spatial Ecologist at WSP, Calgary, AB

AI Summary

WSP is seeking an Intermediate Spatial Ecologist in Calgary, AB, to join their Ecology team. This role involves analysing ecological and spatial datasets, developing GIS and Python workflows, and applying AI and machine learning to environmental decision-making. Candidates require a Master's degree in a related field and 5-10 years of relevant experience in applied spatial ecology or environmental consulting, with strong proficiency in GIS and Python.

The Opportunity at WSP

WSP is actively seeking an Intermediate Spatial Ecologist to join their dynamic Ecology team, a group dedicated to pioneering innovative approaches in ecological assessment and environmental planning. This position offers a unique opportunity to integrate cutting-edge technologies, including artificial intelligence (AI), machine learning, remote sensing, and cloud-based geospatial analytics, with traditional ecological expertise. The goal is to significantly enhance the efficiency, accuracy, and scalability of environmental decision-making across various projects. As part of WSP, you will contribute to a growing team of environmental professionals tackling complex challenges across Canada and beyond, helping clients navigate today's environmental landscape while preparing for future opportunities. This hybrid role can be based in any of WSP's Canadian offices, with remote work considered for the right candidate who brings substantial experience in this specialized field.

Key Responsibilities and Impact

In this pivotal role, the Intermediate Spatial Ecologist will be responsible for analysing complex ecological and spatial datasets, interpreting results to support diverse projects across multiple sectors. A significant part of the impact will involve developing, managing, and executing sophisticated Python and GIS workflows, including spatial modeling, geoprocessing, automation, and precise map production. The successful candidate will also apply machine learning, AI, and advanced analytical approaches to critical areas such as ecological modeling, biodiversity assessment, species distribution modeling, habitat mapping, and comprehensive cumulative effects analysis. Creating clear and compelling data visualizations using Python and/or R is essential for communicating findings effectively. Furthermore, the role requires integrating remote sensing datasets, including satellite imagery, LiDAR, UAV/drone imagery, and Earth observation products, with field-based ecological data to bolster environmental assessments and monitoring programs. Conducting exploratory data analysis and statistical analyses using Python and/or R will be a regular task, alongside preparing detailed technical reports that ensure transparent documentation of analytical methods and results. Collaboration with multidisciplinary teams and contributing to project coordination and execution are also key aspects of this challenging and rewarding position, ensuring robust data management and QA/QC processes across all initiatives.

Qualifications and Expertise

To excel in this role, candidates must possess a Master's degree in Ecology, Data Science, Environmental Science, or a closely related field. A minimum of 5–10 years of relevant experience in applied spatial ecology or environmental consulting is essential. Strong proficiency in GIS platforms, such as ArcGIS Pro or QGIS, including advanced spatial analysis and geoprocessing capabilities, is a core requirement. Demonstrated strong programming skills in Python for data analysis, automation, and visualization are also crucial. The ability to analyze complex ecological data and interpret results within an applied context, along with experience preparing technical reports and communicating analytical results to diverse audiences, is paramount. Previous experience working with large geospatial and ecological datasets, including remote sensing and Earth observation products, is highly valued. Preferred qualifications include proficiency in R for statistical analysis and ecological modeling, experience with statistical modeling approaches such as resource selection functions, and familiarity with cloud-based geospatial platforms. Strong data management, visualization, and reproducibility practices, coupled with excellent communication skills, will set candidates apart, enhancing their overall contribution to WSP's environmental projects.