Research Associate, Data Scientist

Position: Research Associate I
Location: St. Catharines

Important Final date to receive applications Notice  Please note that Workday closes job postings at 11:59pm MST on the day before the posting end date listed above. To ensure your application is received, submit no later than 11:59pm MST the day prior to the posting end date.
Internal NAIT Employees  Current NAIT employees are welcome to apply for this opportunity. To be considered, please submit your application through the NAIT Jobs Hub rather than the external career site.
Temporary Position  This is a temporary position ending on or before August 27, 2027, with the possibility of extension.
Position Title  We are recruiting an Environmental Microbiologist / Bioinformatician at the Research Associate I level to support an applied research program focused on understanding and harnessing microbial processes to address environmental and industrial challenges.

Job Description  The successful candidate will provide advanced expertise in environmental microbiology, metagenomics, microbial genomics, and bioinformatics, with particular emphasis on resolving the composition, function, and metabolic capabilities of complex microbial communities. The Research Associate will lead the analysis and interpretation of metagenomic datasets generated from laboratory cultures, engineered systems, and environmental samples. They will apply advanced bioinformatic approaches to reconstruct microbial genomes, characterize community composition and function, and identify microorganisms, genes, and metabolic pathways associated with environmentally and industrially relevant processes.

A key component of the position will be integrating genomic information with microbiological, chemical, and environmental data and translating research findings into actionable outcomes for industry partners. The successful candidate will work closely with laboratory researchers to guide experimental activities and support the development of microbial cultures, molecular biomarkers, and other applied biotechnology solutions. The position will contribute to research spanning environmental remediation, anaerobic microbial processes, industrial biotechnology, waste treatment, resource recovery, and other emerging applications of environmental microbiology.

Key Responsibilities   Lead bioinformatic analysis of microbial genomic and metagenomic datasets, including sequence assembly, genome reconstruction, annotation, comparative genomics, phylogenetic analysis, and functional characterization.
Integrate genomic and metagenomic data with microbiological, chemical, and environmental datasets to identify microorganisms, metabolic pathways, and molecular biomarkers relevant to applied research objectives.
Collaborate with laboratory researchers to design experiments and translate genomic and microbial insights into actionable outcomes for industry partners.
Develop, implement, document, and maintain reproducible bioinformatic workflows and data‑management practices for large biological datasets.
Analyze, interpret, visualize, and communicate research findings through technical reports, peer‑reviewed publications, conference presentations, and communications with research and industry partners.
Contribute to research project development, grant proposals, new applied research opportunities, and mentorship of research staff, students, and trainees.
Skills & Abilities   Advanced knowledge of environmental microbiology, microbial ecology, microbial genomics, and microbial physiology and metabolism.
Advanced knowledge of metagenomic and microbial genome analysis, including genome reconstruction, comparative genomics, phylogenetics, functional annotation, and metabolic pathway analysis.

Demonstrated proficiency with bioinformatic tools and reproducible workflows for analyzing large microbial sequencing datasets, including Linux/Unix environments and relevant scripting or programming languages.
Strong ability to integrate genomic, microbiological, chemical, and environmental data and translate complex datasets into biologically meaningful conclusions and experimentally testable hypotheses.
Strong quantitative, analytical, troubleshooting, and scientific…