Data Analyst in Omics Full-time

Data Analyst in Omics

As a Data Analyst in the Lê Cao lab, you'll play a crucial role in unravelling complex biological systems through integrative analysis of high-throughput omics datasets. You'll have the opportunity to work on diverse projects, from transcriptomics to single-cell analysis, contributing to cutting-edge research in molecular biology. Working within a multidisciplinary team, you'll develop and implement reproducible analysis pipelines, collaborate on high-impact publications, and potentially contribute to the renowned mixOmics toolkit.

Your responsibilities will include:

• Omics Data Analysis: Conduct end-to-end analytics across diverse omics data types, including quality control, statistical modelling, and multi-omics integration.
• Reproducible Research: Develop and maintain well-documented, version-controlled analysis workflows in R and Python.
• Collaborative Projects: Work closely with lab members and external collaborators, translating scientific questions into analytical plans and delivering interpretable results.
• Knowledge Dissemination: Contribute to publications, present findings, and support mixOmics-related training activities.

You may be a great fit if:

You are a detail-oriented and proactive data analyst with a strong background in bioinformatics or computational biology, demonstrating expertise in statistical programming and a passion for solving complex biological problems through data-driven approaches.

You may also:

• Hold a Masters degree in Bioinformatics, Computational Biology, Statistics, or a closely related discipline.
• Have experience in analysing high-throughput omics data across various types (e.g., transcriptomics, proteomics, metabolomics).
• Demonstrate strong statistical and computer programming skills in R and Python.
• Possess an active GitHub profile showcasing ongoing code development and contributions.

For further information, please refer to the attached PD.

What we offer you:

• Supportive flexible work arrangements underpinned by our commitment to inclusion and well-being
• Progressive, considerate leave provisions to empower your work-life balance
• Salary packaging and access to a range of discounted services including Bupa health insurance
• Health and well-being services including a leading Employee Assistance Program

Your new team – Lê Cao lab, Melbourne Integrative Genomics (MIG)

Lê Cao lab is part of Melbourne Integrative Genomics (MIG), which has been established in 2017 and is an initiative to strengthen the involvement of statisticians in the advances being made in genomics and computational biology. MIG is an interdisciplinary research centre for the application of maths/stats/computing techniques to omics profiling data to help understand genotype-to-phenotype process in humans and other organisms. MIG includes 50+ members, and provides a wide range of opportunities for exciting research collaborations. The Parkville precinct has the greatest concentration of biological and medical research in Australia, and is among the greatest internationally.

The University of Melbourne's School of Mathematics and Statistics is one of Australia's leading mathematics and statistics schools. It has achieved this status through the high quality of its research and teaching programs. The School offers a wide range of subjects to undergraduate and postgraduate students and is involved in aspects of community life that impact on the interests of the School and the discipline.

Be Yourself

The University of Melbourne is an Equal Opportunity Employer and a child-safe organisation. We believe the diversity of our community enriches us all, and we are committed to creating an inclusive and fair workplace where everyone is valued, respected, and empowered to succeed. We welcome applicants from all backgrounds, identities, and experiences.

We're committed to a barrier-free recruitment process and ongoing workplace support, providing reasonable adjustments throughout. We warmly encourage applications from people with disabilities. For assistance, please contact Kim Groizard on +61 3 9035 3218 or at hr-careers@unimelb.edu.au (subject: 'Recruitment Adjustments'). Contact details for other enquiries are located at the bottom of this page.

Aboriginal and Torres Strait Islander Applicants

We aspire to be the University of choice for Indigenous Australians. Our Indigenous strategy, Murmuk Djerring outlines our investment and support for Aboriginal and Torres Strait Islander staff and students. Indigenous applicants are encouraged to connect with “Korin-Korin” our Indigenous Employment & Development team at oied-hr@unimelb.edu.au for further information and support.

Please note: This role requires current valid work rights for Australia. Visa sponsorship is not available.

A Working with Children Check (WWCC) is required for all positions. If successful, we'll guide you through the WWCC application process during onboarding and reimburse the cost.

Join Us and unlock your career potential

• Resume
• Cover Letter outlining your interest and experience

The responses against the following Selection Criteria:

• Masters degree in Bioinformatics, Computational Biology, Statistics, or a closely related discipline (or equivalent professional experience in a relevant setting).
• Experience performing statistical analyses of high-throughput omics data (for example transcriptomics, microbiome, proteomics, metabolomics, single-cell), including appropriate preprocessing and quality control.
• Demonstrated statistical and computer programming experience in R and Python.
• Demonstrated experience in version control on Github and an active GitHub profile evidencing ongoing code development and/or contributions.

For recruitment queries, email Anna at hr-careers@unimelb.edu.au. Include the Position Number and Job Title in the subject line. Please do not send applications to this email address.

For job-specific queries, refer to the contact details in the Position Description.

Applications close: 6 April 2026. 11:55 PM; Melbourne time zone.

Position Description: