Institution/Company:

5 Prime Sciences

Location:

Montreal

, Quebec

 Canada

Job Type:

Staff

Degree Level Required:

PhD

25 days ago Apply now

Description:

5 Prime Sciences, Montréal, QC
5primesciences.com
Work remotely, in or near, Montréal, Canada
Full-time


5 Prime Sciences uses insights from human genetics to accelerate drug development and increase return on investment in the life sciences. Using unique datasets and advanced human genetics analyses, as a spin-out from the Richards Lab at McGill University, 5 Prime has been able to grow rapidly and help large pharmaceutical companies, biotech and venture capital companies.

5 Prime Sciences is seeking a Genetic Epidemiologist or Statistical Geneticist to carry out and interpret results from large-scale genetic data analysis, and assist in the development of analytic workflows. The successful candidate will work with a team of bioinformaticians, clinicians and statistical geneticists at 5 Prime Sciences to develop bespoke genetic epidemiology analyses and run existing workflows that identify novel drug targets and validate them, for our partners in the biotechnology industry.

Responsibilities:

  • Independently perform advanced analyses in genetic epidemiology, including GWAS, ExWAS, PRS, PheWAS, and MR.
  • Identify high-quality genetic datasets for analyses.
  • Contribute to the harmonisation of genetic datasets obtained from different sources and perform QC on these data.
  • Run production-level workflows of various statistical genetics methods using Terra.bio to support target validation.
  • Contribute knowledge of genetic epidemiology to improve internal databases and interfaces for rapid retrieval, visualisation and interpretation of analysed genomic data.
  • Contribute to documentation of computational pipelines, infrastructure and resources.
  • Perform data retrieval and management using Google Cloud Platform, ensuring that data is managed securely and efficiently without loss or corruption.
  • Work collaboratively with the Analytics Team to draft reports and prepare slides for client presentations.
  • Support the development of robust analytical pipelines and perform code review.

Qualifications:

  • Ability to work independently with minimal supervision.
  • Ability to prioritise multiple tasks under tight deadlines.
  • Excellent communication and interpersonal skills.
  • PhD in Genetic Epidemiology, Statistical Genetics or related field with expertise in the following areas:
    • Demonstrated exposure to human genomic datasets is required (GWAS, WES, WGS, Mendelian Randomisation, etc.).
    • Experience working with large-scale genomic datasets, e.g. UKBiobank, 1000 Genomes etc.
  • Significant bioinformatics experience is required with expertise in the following areas:
    • Experience in running software in a Linux or UNIX-like environment including shell scripting and common command-line tools.
    • Demonstrated proficiency in R is required.
    • Demonstrated proficiency in Python is an asset.
  • Expertise in cloud computing and software development is an asset:
    • Working familiarity with cloud computing environments (GCP, AWS, DNANexus, Terra, etc.).
    • Knowledge of software development methodology (version control, testing, documentation).

Additional Information:

Please note – we recognize that not all candidates may meet the entire Profile listing above. We’re eager to train the right candidates for this role and encourage those who meet at least two thirds of the criteria to apply.

Languages
The applicant will have high-level proficiency in English reading, writing and presentations. French language skills are an asset.

Equal Opportunity
We are an equal opportunity employer. All applicants will be considered without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

Eligibility Requirements
Qualified candidates will be considered from any country. Where appropriate, 5 Prime Sciences can arrange immigration to Canada.

Keywords:

genetic epidemiology

statistical genetics

genomics

gwas

exwas

mendelian randomization

polygenic

prs

python

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