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Entry-Level Data Analyst

Nomi Trading and Logistic Services

Melbourne, Victoria, Australia (Hybrid) 兼职

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职位空缺
1
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1 小时前
工作模式
杂交种
学历
Progress toward or completion of degree in Data Science, Statistics, Mathematics, Business, or related field
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职位描述

Role Overview

This part-time Data Analyst position, located in Melbourne, VIC, offers a hybrid work environment combining onsite presence with flexible work-from-home days. The primary focus is on gathering, cleansing, and organizing operational and logistics data from various sources to enhance reporting and performance measurement.

Key Responsibilities

  • Create and maintain basic dashboards to visualize data effectively.
  • Generate periodic summaries of data to support monitoring efforts.
  • Assist with data modeling activities aimed at improving forecasting accuracy and optimizing logistics resources.
  • Conduct exploratory data analysis to detect patterns, trends, and operational inefficiencies.
  • Prepare clear documentation of findings and present actionable insights to internal teams.
  • Collaborate closely with operations, finance, and management departments to ensure integrity and usefulness of data.

Candidate Profile & Qualifications

  • Strong analytical thinking combined with fundamental data analytics competencies to interpret business and operational datasets.
  • Foundational knowledge of statistics to support quantitative assessment and performance tracking.
  • Basic experience or understanding of data modeling techniques for forecasting and logistics optimization.
  • Effective communication skills essential for sharing insights and working across different teams.
  • Familiarity with spreadsheet software such as Excel or Google Sheets; experience with data visualization or BI tools is a plus.
  • Preferred candidates will have completed or be pursuing a degree in Data Science, Statistics, Mathematics, Business, or related disciplines.
  • Ability to work independently while managing time efficiently in a part-time schedule, with strong attention to detail in handling data tasks.

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