Data Analyst (Bioinformatics, Multi ‘Omics Research)

Job Summary The Edwin S. H. Leong Centre for Healthy Aging (ELCHA) is seeking a highly motivated and ambitious Data Analyst for the Canadian Microbiome Mapping Initiative (CMMI). The primary goal of the CMMI is to develop an integrated multi ‘omics platform to investigate the biological mechanisms and pathways by which the microbiome influences health and disease. This will include investigations into healthy aging as well as validation of the platform in clinical contexts, beginning with Parkinson’s disease. The successful candidate will serve as a data manager to liaise between the project’s different data generating and database development teams and facilitate the integration, analysis, and interpretation of datasets.

Organizational Status The CMMI is a large, collaborative initiative involving investigators in the ELCHA, other UBC research centres, and external collaborators.

The position works under the scientific and technical guidance of the CMMI Data Integration and Analysis Committee (Dr. Raymond Ng, Dr. Emilia Lim, and Dr. Keegan Korthauer), and reports to the CMMI Project Manager for day-to-day operations, prioritization, and project delivery.

The position also works in close collaboration with CMMI investigators, bioinformaticians, laboratory personnel, and research staff. The position will provide technical supervision and mentorship to computational biology trainees, technicians, and graduate students, as appropriate.

Responsibilities

• Pipeline development: Design, develop, implement, and maintain reproducible multi-omics data integration pipelines, including data processing, harmonization, and statistical analysis workflows.
• Data translation: Coordinate technical data activities across laboratories and omics groups; works with investigators, bioinformaticians, and technicians to identify available data and analytical need; and translate these into technical specifications for database and infrastructure development. Consult key stakeholders to capture data management requirements, such as data planning, collection, processing and compliance, pertaining to the Faculty of Medicine data platforms, software, and tools in support of good data management practices.
• Data quality: Perform quality control and ensure the integrity of research datasets, collaborating with contributing laboratories to resolve technical issues.
• Analysis: Conduct statistical, bioinformatics, and AI/ML-based analyses to interpret relationships between datasets, health, and disease.
• Software evaluation: Evaluate and implement tools and software for data analysis, visualization, and integration.
• Technical data support: Serve as a technical data partner, assisting investigators, trainees, and staff with data processing, analysis, and workflow implementation under project guidance.
• Documentation: Produce and maintain technical documentation, including pipeline workflows, algorithms, QC procedures, and SOPs to ensure reproducibility.
• Communication: Communicate technical progress, analytical findings, and workflow updates to collaborators through meetings, presentations, and contributions to manuscripts.
• Performs other related duties as required.

Minimum Qualifications

• Post-graduate degree in Statistics. Minimum of two years of related experience in research analysis, or the equivalent combination of education and experience.
• Willingness to respect diverse perspectives, including perspectives in conflict with one’s own.
• Demonstrates a commitment to enhancing one’s own awareness, knowledge, and skills related to equity, diversity, and inclusion.

Preferred Qualifications

• Master’s degree in bioinformatics, computational biology, statistics, computer science, or a closely related field preferred.
• 2-3 years of relevant experience in data integration, bioinformatics, or computational analysis in research preferred.li>
• Proficiency in R, Unix/Linux environments, shell scripting, databases, and version control (Git).
• Strong written and verbal communication skills.
• Ability to work independently while collaborating with multidisciplinary teams.
• Demonstrated ability to effectively engage with colleagues and the researcher community.
• Experience with multi-omics datasets and integration across modalities.
• Familiarity with AI/ML applications for data analysis.
• Experience in automated pipeline design and maintenance.
• Familiarity with visualization and interactive tools (RShiny, Plotly, Power BI).
• Knowledge of molecular and cellular biology an asset.
• Ability to use sound judgment to make decisions.