Statistical Ecologist

Competition 1861

This position is a part of the Non-Academic Staff Association (NASA).

This position has a term length of 2 years from the date of hire and offers a comprehensive benefits package which can be viewed on our Benefits Overview page.

Location - This position offers a flexible work arrangement, allowing for a combination of remote and/or in-office work based on work preferences. We welcome applicants from across Alberta. In the event that you need to work on-site, you may have the flexibility to attend either the Edmonton or Calgary offices based on your location and convenience. Preference will be discussed during the recruitment process.

Working at the University of Alberta

The University of Alberta acknowledges that we are located on Treaty 6 territory, and respects the histories, languages and cultures of First Nations, Métis, Inuit and all First Peoples of Canada, whose presence continues to enrich our vibrant community.

The University of Alberta is a community of knowledge seekers, change makers and world shapers who lead with purpose each and every day. We are home to over 14,000 faculty and staff, more than 40,000 students and a growing community of 300,000 alumni worldwide.

Your work will have a meaningful influence on a fascinating cross-section of people — from our students and community members, to our renowned researchers and innovators, making discoveries and generating solutions that make the world healthier, safer, stronger and more just. Learn more.

Working for the Alberta Biodiversity Monitoring Institute (ABMI)

The Alberta Biodiversity Monitoring Institute (ABMI) is dedicated to tracking changes in biodiversity across the entirety of Alberta, now and in the future, and provides the public with the data, knowledge, and tools to make informed decisions about the state of the environment.

Position 

The Statistical Ecologist works in ABMI’s Science Centre and is primarily responsible for analyzing data to support environmental management in Alberta and elsewhere in Canada. The position will contribute to the advancement of species distribution modelling and will further understanding of how model reliability affects risks associated with conservation decisions and applications.

Duties 

• Develops, tests, and implements species distribution models using a variety of statistical approaches.
• Collaborates with statistical experts to interpret model reliability (uncertainty, data scarcity, and expert evaluation) and determine how it affects risks associated with conservation decisions and applications relating to species at risk and migratory birds.
• Collaborates with taxonomic experts to understand taxa specific data challenges and limitations, to support the development of monitoring protocols and to solicit feedback on species model evaluations.
• Develops communication tools & guidance on how to use model reliability as an input for assessments of biodiversity co-benefits, cumulative effects, impacts, and other decisions pertaining to species at risk and migratory birds.
• Develops methods to incorporate model reliability into an adaptive modelling framework to improve species distribution models.
• Participates and leads in the writing of research papers and the preparation of presentation and reports; collaboration with academics and other members of the science community is expected.

Minimum Qualifications

• MSc in statistics, ecology, or a related natural resource management field.
• Thorough understanding of statistical theory and application.
• Greater than 3 years working with R or Python programming language.
• Strong written and oral communication skills.

Preferred Qualifications

• PhD in statistics, ecology, or a related natural resource management field.
• Experience building spatially predictive species distribution models.
• Strong knowledge of model validation and reliability.
• Experience working with large datasets.
• GIS experience in R or other platforms.
• Knowledge of bird species and populations in Canada.
• Experience leading multidisciplinary groups of experts in decision making processes.
• Experience communicating complex statistical concepts to non-technical audiences.
• Record of peer-reviewed publication.