Bioinformatics​/Data Science Analyst

Job Details

• Site:
  Princess Margaret Cancer Centre
• Department:
  Research
• Reports to:
  
  Principal Investigator
• Union:
  Non-Union
• Number of Vacancies: 1
• New or Replacement Position:
  New
• Salary Range: $35.35 - $53.03 per hour
• Hours:
  
  37.5 hours per week
• Shifts:
  
  Monday - Friday, Days
• Status:
  Temporary Full-time (1-year contract)
• Closing Date:
  August 16, 2026

A Senior Bioinformatics Analyst position is available in Toronto, Canada, in the laboratories of Dr. Tomohiro Aoki and Dr. Robert Vanner at the Princess Margaret Cancer Centre, University Health Network, one of the top five cancer research centres in the world. The research focus is basic/translational research in cancer immunology and cancer genomics in lymphoid malignancy and clonal hematopoiesis. With access to world‑class computational infrastructure and collaboration with leading scientists, the candidate will help organise, plan, analyse, and interpret large genomic cohort analyses that are currently ongoing.

• Design and execute experiments to study the pathogenesis, molecular mechanism and treatment resistance mechanism, and tumour‑microenvironment interaction in lymphoid malignancy.
• Analyse and interpret molecular analyses from DNA/RNA sequencing data (including single‑cell sequencing, whole genome sequencing, circulating tumour DNA analyses, methylation profiling).
• Analyse and interpret spatial analyses (e.g. protein level and transcriptional level).
• Develop and oversee high‑throughput data pipelines, including data from many new single‑cell technologies such as scRNA‑seq, scATAC‑seq, sc Multiome‑seq, and Visium/Cos Mx spatial transcriptomics.
• Analyse any sequencing data using established pipelines.
• Develop new computational methods to integrate and interpret multi‑omics data.
• Assist and collaborate with internal and external researchers in interpretation of sequencing data.
• Contribute to the codebase, development, and support of open‑source software packages.
• Help to manage lab servers and computational infrastructure.
• Document and present results in written or oral reports to other lab members.
• Work collaboratively with other research team members and external collaborators.
• Participate in multidisciplinary projects combining biochemistry, structural biology, and computational methods.
• Stay updated with the latest research in cancer genomics, cancer immunology and lymphoma biology.
• Review and summarise relevant scientific literature to inform experimental design and data interpretation.
• Prepare manuscripts for publication in scientific journals.
• Present research findings at national and international conferences, seminars, and workshops.
• Assist in writing grant proposals to secure funding for ongoing and future research projects.
• Mentor and supervise graduate and undergraduate students involved in related research projects.
• Provide guidance on experimental techniques and data analysis.

• B.Sc., M.Sc. or Ph.D. in bioinformatics, computer science or a recognised equivalent in Health and/or Science‑Related Discipline or a related field.
• At minimum, 1 year related experience.
• Programming skills in R, Python, BASH, Tensor Flow or similar.
• Experience working in a Linux environment and the use of high‑performance computing systems (e.g. Slurm) for analysing large datasets.
• Adhering to best practices for software development (version control, Git Hub).
• Demonstrated exposure to NGS data analysis and data types (FASTQ, BAM, VCF, etc.) including analysis of targeted, exome or genome sequencing data.
• Hands‑on experience with cancer genomic analyses including WGS, RNA‑seq, ATAC‑seq, single cell RNA‑seq, or similar (preferred).
• Experience in cancer genomic study (preferred).
• Team oriented with excellent written and verbal communication skills.
• Training in statistics or machine learning (preferred).
• Ability to work effectively in a collaborative research environment.
• Experience in mentoring students and junior researchers.
• Solid skills in statistical analysis (ANOVA, regression, clustering, phylogenetics, survival).
• Experience with machine learning and data modelling (asset).
• Excellent communication skills.
• Willingness to work in a team environment.