Industry: Education - Excluding Post Secondary - Others
Type: Full Time
What you’ll do
Contribute to ongoing therapeutic development programs by analyzing clinically relevant disease subtypes to help understand the mechanisms of action of our targets/drugs and identify predictive and prognostic genetic biomarkers.
Lead the effort to identify, evaluate and curate public genetic and genomics data related to disease subtypes and drug response.
Design/implement novel methods and tools to analyze human genetic and genomic data for both biomarker and target discovery.
Work collaboratively with biomarker scientists, drug discovery scientists, computational biologists, statistical geneticists, and drug development scientists and clinicians.
What you’ll bring
Ph.D. in Computational Biology, Statistics, Computer Science or a related quantitative field
1-5 years' post-Ph.D. experience, reflecting a strong record of achievement either through publications or successful biotech/pharmaceutical industry accomplishments in health sciences or drug development
Ability to communicate clearly and effectively especially to non-specialists
Ability to work well on cross-functional teams
Experience developing robust data analysis software in R and/or Python
Expertise in several of the following: RNAseq, WES/WGS, mass spectrometry, regression modeling, and/or machine learning
Understanding and expertise in human genetics, GWAS, fine mapping, QTLs
Knowledge of biology in at least one disease-relevant area (e.g., immunology, oncology, metabolic or cardiovascular disease, etc.)