Data Analyt (Entry / Junior)

Role Description The Data Analyst (Entry / Junior) will support research projects by collecting, cleaning, and analyzing experimental and observational data generated within the laboratory. Day-to-day responsibilities include preparing datasets, running statistical tests, creating data models, and generating visualizations and summaries to help interpret findings related to agricultural traits and complex diseases. The role involves collaborating with researchers to translate scientific questions into analytical tasks, documenting methods and results, and contributing to reports, presentations, and manuscripts. This is a part-time position based in Sydney, NSW, with a hybrid work arrangement that combines on-site work in the laboratory with some work from home. The analyst will receive guidance from senior scientists and gain exposure to practical applications of genomics and proteomics data.

Qualifications

• Strong analytical skills and foundational knowledge in data analytics to interpret research data and identify patterns.
• Understanding of statistics, including basic statistical tests, data distributions, and familiarity with statistical software or programming languages (e.g., R, Python, or similar).
• Experience or coursework in data modeling, such as building simple predictive models, regression analyses, or classification approaches.
• Effective communication skills to present results clearly, collaborate with multidisciplinary teams, and document methods and findings.
• Relevant academic background, such as ongoing or completed studies in data science, statistics, bioinformatics, computer science, mathematics, or a related field.
• Basic proficiency with data analysis tools (e.g., spreadsheets, statistical packages, or scripting languages) and data visualization software.
• Ability to work both independently and in a team, manage multiple tasks in a research setting, and adapt to evolving project needs.
• Interest in genomics, proteomics, agriculture, or disease biology; prior exposure to biological data or laboratory environments is an advantage but not required.