Student Assistant (Talent Management, Part-time, Copenhagen)

Job summary

This position involves supporting the analytics and operational aspects of global Talent Management processes within a dynamic environment. The role is suited for a student interested in data and its impact on business decisions, focusing on analytical thinking and structured methodologies.

Qualifications

• Currently pursuing a Bachelor's or Master's degree in Business, Engineering, Economics, Data Analytics, HR, Social Sciences, or a related field.
• Strong analytical abilities with experience in data modeling, particularly in Excel; familiarity with Power BI or similar tools is a plus.
• Capable of organizing data, identifying inconsistencies, and managing large datasets systematically.
• Proficient in English, comfortable in a global setting.
• Interest in the role of data in decision-making related to personnel and business outcomes.

Responsibilities

• Prepare, validate, and maintain people data for global People Review cycles, ensuring high quality and consistency.
• Assist the Head of Group Talent Management with data analysis, dashboards, and insights for talent and succession planning.
• Support operational tasks related to Talent Management processes, including People Review and succession planning.
• Organize and maintain datasets, templates, and documentation for clarity and reusability.
• Identify data trends and improvement opportunities, suggesting enhancements to data handling processes.
• Collaborate with teams in People & Culture and People & Data Analytics for data and process coordination.

Skills

• Curiosity and eagerness to uncover insights from data.
• Detail-oriented and reliable in data management tasks.
• Ability to work independently while seeking guidance and feedback.
• Strong collaborative skills, comfortable engaging with various stakeholders.

Education

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

Tools

• Proficiency in Excel; knowledge of Power BI or similar analytics platforms is advantageous.