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A leading company in the pharmaceutical field is seeking innovative Computational Biologists to join their Toronto team. Successful candidates will analyze biological data, develop algorithms for drug discovery, and work alongside diverse scientific professionals to drive impactful projects. Enthusiasts of data science and biology are encouraged to apply.
Toronto
Permanent Job
Are you a data-driven scientist passionate about turning complex biological data into meaningful insights? We are currently looking for Computational Biologists to join our innovative and collaborative team in Toronto! If you're open to new challenges and excited to contribute to impactful projects in the pharmaceutical space, we’d love to hear from you.
Responsibilities:
Analyze large-scale biological and multi-omics datasets (genomics, transcriptomics, proteomics, etc.)
Develop and apply computational models, pipelines, and algorithms to support drug discovery and development
Collaborate with cross-functional teams including bioinformaticians, data scientists, and wet lab researchers
Interpret results and present findings to scientific and non-scientific stakeholders
Contribute to the design of experiments and data collection strategies
Ensure data quality, reproducibility, and compliance with scientific and regulatory standards
Requirements:
PhD or Master’s degree in Computational Biology, Bioinformatics, Genomics, or a related field
Proven experience applying computational biology methods in a pharmaceutical or biotech setting
Proficiency in programming languages such as Python, R, or Perl
Experience with high-throughput data analysis and visualization tools
Solid understanding of molecular biology and biological systems
Strong communication skills and ability to work collaboratively across scientific teams
Amaris Consulting is proud to be an equal opportunity workplace. We are committed to promoting diversity within the workforce and creating an inclusive working environment. For this purpose, we welcome applications from all qualified candidates regardless of gender, sexual orientation, race, ethnicity, beliefs, age, marital status, disability or other characteristics.