Computer Specialist - Biostatistics Job Saskatoon area,Saskatchewan Canada,Research/Development

Tenure:

Continuing

Language Requirements: English

Work arrangements:

Due to the nature of the work and operational requirements, this position will require full-time physical presence at the NRC work location identified.

At the NRC, we recognize that Indigenous candidates may have important connections to their communities and you may be eligible for an exception to this work arrangement. Alternative work arrangements may also be considered to accommodate candidates as required. To learn more about these options, please contact the NRC Hiring team using the contact information below.

Discover the possible

Anything is possible at the NRC, named in 2025 one of Canada’s Top Employers for Young People, Top Employer in the National Capital Region and Forbes Canada’s Best Employers!

As Canada’s leading research and technology organization, our world-renowned research pushes the boundaries of science and engineering to make the impossible, possible. Every day we explore new ideas through innovative research and help companies discover possibilities that impact Canada’s future and the world.

At the NRC, you’ll also discover new possibilities. Our supportive workplace fosters a culture of creativity, welcoming fresh perspectives and innovation at all levels. We value teamwork. You’ll collaborate across multiple fields and with the brightest minds to find creative solutions. Most importantly, you’ll discover what’s possible within you as you grow, make valuable contributions and progress in your professional journey.

From ground-breaking discoveries to a life-changing career, discover your possible at the NRC.

The role

This position will be instrumental in supporting and advancing biostatistical research and bioinformatics projects within a collaborative environment, focused on analyzing large-scale biological data. The incumbent will manage the development and implementation of advanced statistical models and bioinformatics tools, with an emphasis on applying innovative computational approaches to complex biological datasets. This includes tasks such as integrating multi-omics data, conducting multivariate and Bayesian analyses, and developing web-based platforms for data visualization and manipulation.

The successful candidate will collaborate with research officers, and data analysts to design and execute data-driven projects, contributing to the development of novel statistical methodologies and bioinformatics solutions. As part of a multidisciplinary team, they will ensure that the statistical and computational pipelines are optimized for high-throughput biological data, enabling breakthroughs in areas such as association mapping, genomic selection, and predictive breeding.

The role requires not only technical expertise in bioinformatics and statistical modeling but also effective communication with diverse stakeholders.

Screening criteria

Applicants must demonstrate within the content of their application that they meet the following screening criteria in order to be given further consideration as candidates:

Education

, or

For information on certificates and diplomas issued abroad, please see Degree equivalency

Experience

Demonstrated experience in applying advanced statistical methodologies to large, complex biological datasets

Hands-on experience with various statistical methodologies, including dimension reduction, multivariate analysis, Bayesian inference and linear and non-linear modeling

Experience in data mining, integrative data analysis, data visualization, and the development of interactive computational environments

Experience with the development and implementation of bioinformatics tools, algorithms, pipelines and software solutions including database resources in the area of bioinformatics

Experience developing web-based applications for data analysis, manipulation, and visualization

Experience working with plant, microbiome and/or agricultural datasets would be considered an asset

Experience with the following data types would be considered an asset: short-read and long-read sequencing, RNA-seq, single-cell sequencing, proteomics, targeted and untargeted metabolomics, and datasets associated with…