Student Assistant – Supply Chain Data & Analytics (On-site, Lyngby)

Job summary

This position offers hands-on experience in supply chain data and analytics, supporting a global organization in enhancing its data-driven decision-making processes. The role involves collaboration with a small team of analysts and student assistants to improve data quality and transparency across various supply chain domains.

Qualifications

• Currently enrolled in a Bachelor’s or Master’s program in Supply Chain Management, Operations Management, Industrial Engineering, Data Science, Business Analytics, Economics, or a related field, with at least one year remaining in studies.
• Strong analytical mindset with interest in data-driven decision-making and supply chain processes.
• Basic experience with data and analytics tools such as Excel, KNIME, Snowflake, Power BI, SQL, or similar.
• Ability to structure information and communicate insights clearly to stakeholders.
• Comfortable learning new systems and collaborating across teams in English.

Responsibilities

• Support data collection, validation, and preparation across supply chain domains using tools like KNIME, Snowflake, and Power BI.
• Assist in developing and maintaining dashboards, reports, and analytical insights in Power BI.
• Document data models, definitions, and process flows to enhance transparency and data quality.
• Perform ad hoc analyses to support decision-making for supply chain teams.
• Support automation and improvement initiatives within data and analytics workflows.
• Collaborate with supply chain, IT, and analytics colleagues across regions.

Skills

• Familiarity with Power BI beyond basics (data modeling, DAX, advanced visuals) is a plus.
• Experience with SQL querying and/or data pipelines in tools like KNIME is advantageous.
• Understanding of supply chain data concepts (KPIs, master data, process flows) is beneficial.
• Interest in improving workflows through automation and continuous improvement.

Education

• Enrollment in a relevant Bachelor’s or Master’s program is required.

Tools

• Experience with data analytics tools such as KNIME, Snowflake, Power BI, and SQL is preferred.