Computational Biologist - Statistical Geneticist

Institution/Company:
Hoffman La Roche
Location:
Mississauga, ON, Canada
Job Type:
  • Staff
Degree Level Required:
PhD
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Computational Biologist - Statistical Geneticist

Seeking an interdisciplinary scientist with experience in computing and statistical genetics to perform scientific data analyses in partnership with the Human Genetics Department of Genentech Research and Early Development (gRED) from the Roche Canada office in Mississauga, Ontario.

The successful candidate will evaluate, prototype, and implement cutting-edge statistical methods (and pipelines) to analyze large-scale genetic, genomic, and proteomic datasets. The candidate will also work with a team of scientific software engineers who will support the development of these methods into production-scale pipelines. Areas of focus will include GWAS analysis methods (fine-mapping, eQTL/pQTL co-localization, local ancestry based mapping); analysis of functional genomic data (epigenetic data and genome-wide CRISPR screen analysis) and integration with GWAS summary statistics; and analysis of gene-networks and pathways.

Responsibilities:

Evaluate alternative analytical methods relevant to the needs of Genentech Human Genetics scientists. Implement working analysis pipelines that run existing/published tools. Develop new tools in R or python under the guidance of Genentech Human Genetics scientists Use git to track code, document the code along with examples of running the code, and provide demos to the analysts in HG group who will be using the pipelines Study publications related to the tools to gain deeper understanding of how they work Implement methods and pipelines at scale to deliver statistical analyses Interact with software engineers, contribute to pipeline design documentation, clarify implementation requirements, and guide them in developing production level versions of the pipeline for wider use

Qualifications:

PhD in Human Statistical Genetics (or related field). MSc and significant experience also accepted. Postdoctoral experience preferred. Ability to produce high-quality work with minimal supervision; this includes meeting reasonable deadlines and making sensible independent decisions. Unix/Linux proficiency. Fluent in R, Python, and shell scripting. Familiarity with current Human Genetics methods and literature.

Desired Skills and Experience

Familiarity with C++. Experience with Bioconductor libraries. History of scientific authorship. (First, middle, and senior author articles all beneficial.) Experience with version control (git). Experience with High Performance Computing (eg. Slurm). In your cover letter, please highlight publications/experience with GWAS, functional genomics analysis, and gene networks + pathways. The ability to read and interpret Human Genetics publications to understand the latest tools. Excellent communication, advanced English reading, writing, and speaking skills. Desire to learn more about Human Genetics, Bioinformatics, and Biology

Additional Information

Please submit your full academic CV with publication history and cover letter.

In your cover letter please highlight key scientific publication(s) (or experiences) that emphasize your personal biological and computational scientific contributions to the Statistical Genetics field.

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