Computational Biologist

Job Description
The Advanced Artificial Intelligence Research Laboratory at Genuity Science is seeking a highly motivated Computational Biologist to help pioneer the use of AI/ML in the biomedical sciences. The candidate will work on data generated from secondary/tertiary analysis pipelines and use AI/ML, probabilistic programming, and other statistical genomics approaches to analyze various large-scale omics data sets to better understand disease etiology, identify novel drug targets, and discover biomarkers for use in precision medicine.

Given the multi-disciplinary nature of the position, strong collaboration and communication skills are expected.

Essential Qualifications:

• M.Sc. in Engineering, Computational Statistics, Computer Science, Biostatistics, Bioinformatics, or related field with a minimum of 2-years of related industry and/or academic experience
• Experience in machine learning, deep learning, statistical methodology, predictive modeling and algorithm development
• Familiar with NGS data analysis, using common bioinformatics tools (BWA, STAR, Picard, GATK etc.), and knowledge of publicly available genomics databases (i.e. ENCODE, GEO, TCGA, CCLA)
• Advanced programming skills with fluency in at least Python and/or R, with extensive experience using modern machine learning and deep learning libraries (TensorFlow, PyTorch, Edward, sklearn, caret, etc.)
• Proven ability to design and code production grade machine AI/ML applications, along with a strong ability to visualize ‘big data'
• Ability to work on high-performance computing system and manage cloud computing environments (e.g. AWS) with experience working with GPUs
• Strong communication and presentation skills with the ability to translate and communicate results to individuals of diverse backgrounds

Preferred Qualifications:

• Ph.D. and postdoctoral training in Engineering, Computational Statistics, Computer Science, Biostatistics, Bioinformatics, or another related field
• Understanding of modern genomics analysis including RNA-seq, single cell RNA-seq, DNA methylation, variant analysis, etc.
• Development and application of digital pathology and natural language processing algorithms
• Working knowledge of biology (oncology, immunology, autoimmunity, etc.) and experience in drug target identification
• Up-to-date knowledge of the fast-moving AI/ML literature