Data Analyst (Entry / Junior)

Role Overview

The Data Analyst at an entry or junior level will assist in research initiatives through the management and analysis of experimental and observational datasets generated in the laboratory. The position involves preparing and cleaning data, conducting statistical analyses, modeling, and producing visual summaries to elucidate insights on agricultural traits and complex diseases.

Key Responsibilities

Daily tasks include collaborating with research personnel to convert scientific inquiries into analytical workflows, applying statistical tests, creating predictive models, visualizations, and summarizing results for interpretation. The analyst will document methodologies and findings and contribute to research outputs such as reports, presentations, and scientific manuscripts.

Work Arrangement

This part-time role is based in Sydney, NSW with a flexible hybrid schedule combining on-site laboratory presence and work-from-home options. The position offers mentorship from senior scientists and exposure to real-world applications of genomics and proteomics datasets.

Qualifications and Skills

• Strong foundational analytical capabilities and understanding of data interpretation to detect meaningful trends.
• Familiarity with statistical concepts including test applications, distributions, and experience with statistical programming languages such as R or Python.
• Some exposure or coursework in data modeling techniques like regression, classification, or predictive modeling.
• Effective communication abilities to clearly convey analytical outcomes and work collaboratively across disciplines.
• Relevant educational background, such as ongoing or completed studies in data science, statistics, bioinformatics, computer science, mathematics, or related disciplines.
• Basic competency with data analysis applications—such as spreadsheets, statistical software, or scripting languages—and data visualization tools.
• Capacity to work independently and as part of a team, managing multiple research tasks efficiently and adapting to project changes.
• Interest in biological domains like genomics, proteomics, agriculture, or disease biology; prior exposure to biological data or lab environments is beneficial but not mandatory.