Institution/Company:

Imagia Canexia Health

Location:

Vancouver

, British Columbia

 Canada

Job Type:

Staff

Degree Level Required:

PhD

371 days ago Apply now
Description:

Computational Biologist

Permanent full-time position

About the position

Imagia Canexia Health is currently on the lookout for an experienced Computational Biologist. The candidate will be part of our Computational Science team, which leads the bioinformatics research and development program at Imagia Canexia Health (ICH). Reporting to the Sr. Manager of Computational Science, you will develop software and methodologies to perform analysis of genomic sequencing data using leading-edge DNA sequencing technologies to detect genetic markers for cancer treatment and prognosis. You work independently as well as together with software programmers, other computational biologists, clinical geneticists, and laboratory personnel.

This is a full-time employment opportunity based in our Vancouver office with the possibility of remote work in hybrid mode.

To be eligible for this position, an applicant must be legally entitled to work in Canada.

What you’ll be accountable for :

As a member of our team, you will:

  • Evaluate and screen externally developed tools for potential internal adoption.
  • Work closely with the laboratory team to develop bioinformatics pipelines and methodologies to detect genetic markers from novel assay developments.
  • Provide analysis and interpretation of genomic data.
  • Work closely with the software team to integrate the novel pipelines in the ICH Informatics Platform and to optimize the pipelines.
  • Evaluate the algorithm performance in commercial and clinical samples.
  • Develop, manage, and document genome sequencing data analysis pipelines and other algorithms and statistical models.
  • Keep up-to-date with novel methods and algorithms for variant analysis in cancer genome sequencing data.

About you

The person our team is looking to welcome

The preferred candidate generally holds a Ph.D. in computational biology, computer science, ML/AI, applied mathematics, or bioinformatics. Any equivalent combination of education and experience could also be considered.

Requirements

  • Proven track record: of strong publications that you are ready to show us.
  • Research experience: Prior experience working in computational biology.
  • NGS: Experienced in advanced-level genomic and/or next-generation sequencing analysis or methods development.
  • Commendable analytical skills: Experience in developing and implementing computational biology algorithms. Understanding of computational techniques such as machine learning and deep learning, Bayesian approaches, probabilistic models, and elementary statistics.
  • Attention to detail: Uncompromisingly meticulous with the execution of analysis.
  • Linux/UNIX: Experienced in working with command-line interfaces.
  • Coding: Proficient with Python (desirable) or other coding languages.
  • Tech savvy: Experienced for 3+ years with standard NGS analysis tools such as samtools, alignment software.

Nice-to-haves : 

  • Experience in cancer genomics
  • Experience in analysis of Targeted Sequencing Data and/or Whole Genome Sequencing Data
  • Experience with GitHub and cloud-based computing such as AWS or Azure

About Us

We believe everyone with cancer should have the same fighting chance to survive and thrive. We work in a rapidly evolving field that attracts smart, talented people who are committed to making a difference for cancer patients. But not everyone has access to the latest advances. People who join us are committed to bringing equity to critical aspects of cancer care. We are a lean and driven team building on this vision from the ground up. You’re a self-starter who can pitch in right away by deploying your own expertise to make this shared vision a reality.

We are an Equal Opportunity Employer. We are committed to creating an inclusive environment for all employees.

Keywords:

Linux

GitHub

Python

Coding

Computer Science

algorithms

data structures

complexity analysis

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