Principal Bioinformatics Scientist I

Roche•Mississauga, ON

5d•Onsite

About The Position

At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections, where you are valued, accepted and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters. The Position We are seeking a highly skilled and experienced Principal Bioinformatics Scientist I specializing in Next Generation Sequencing (NGS) to join our research team. As a Computational Biologist in NGS, you will lead and contribute to cutting-edge projects focused on analyzing and interpreting NGS data, playing a pivotal role in advancing our understanding of complex biological systems, genetic variations, and disease mechanisms. Your expertise and leadership will drive the development and application of innovative computational methods in NGS data analysis.

Requirements

Ph.D. in Computational Biology, Computer Science, Mathematics, Statistics, Bioinformatics, Genomics, or a related field.
5-7 years of extensive NGS Data Analysis experience in computational biology or bioinformatics, with a strong focus on NGS data analysis and interpretation.
Proficiency in NGS Tools and Pipelines: In-depth knowledge and hands-on experience with commonly used NGS data analysis tools and pipelines, such as BWA, GATK, SAMtools, Picard, and variant annotation databases (e.g., dbSNP, ClinVar).
Programming Skills: Proficiency in programming languages commonly used in computational biology, such as Python, R, or Perl.
Experience with shell scripting and working in a Linux/Unix environment.
Collaboration and Communication: Demonstrated ability to collaborate effectively

Nice To Haves

Postdoctoral Experience: Prior postdoctoral or industry experience in computational biology, bioinformatics, or a related field.
Epigenomics Expertise: Expertise in analyzing and interpreting epigenomic datasets, including DNA methylation, histone modifications, chromatin accessibility, and other relevant epigenetic marks.
Familiarity with relevant databases, annotation resources, and tools.
Machine Learning and Predictive Modeling: Experience with machine learning techniques, statistical modeling, and data mining approaches applied to translational bioinformatics and clinical decision support.

Responsibilities

Apply advanced computational methods and algorithms to analyze and interpret NGS datasets, including whole-genome sequencing, exome sequencing, transcriptome sequencing, ChIP-seq, and other relevant NGS applications.
Perform quality control, data preprocessing, alignment, variant calling, and annotation.
Utilize your expertise in variant calling and analysis to identify and interpret genetic variations, including single nucleotide variants (SNVs), insertions, deletions, structural variants, and copy number variations.
Assess variant functional impact and provide insights into disease mechanisms.
Integrate NGS data with other omics datasets (e.g., transcriptomics, epigenomics) to gain a comprehensive understanding of biological systems.
Apply integrative approaches to uncover relationships between genomic alterations, gene expression, and epigenetic modifications.
Develop and optimize computational algorithms, statistical models, and bioinformatics tools for NGS data analysis, addressing specific research questions and improving accuracy, efficiency, and reproducibility of the analyses.
Visualize and communicate NGS data and analysis results effectively using appropriate visualization techniques and tools.
Present findings to both technical and non-technical audiences, including internal stakeholders and external collaborators.
Collaborate closely with experimental biologists, geneticists, and clinicians, providing computational expertise, data analysis support, and guidance in experimental design.
Lead and oversee computational biology projects involving NGS data analysis, from experimental design to data interpretation.
Set project objectives, develop research plans, coordinate team members, and ensure timely project completion while maintaining high-quality standards.