Thesis in Supply Chain Data Analysis for Security Risk Detection

• You analyze extensive supply chain data to identify patterns and precisely define relevant deviations.
• You define a subset of the data to be examined and define its limits appropriately to enable a focused investigation.
• You conduct a detailed investigation of the defined subset, for example by analyzing inventory discrepancies in selected storage locations.
• You provide a sound assessment of the suitability and efficiency of various monitoring mechanisms for use in a global corporation.
• You summarize your results in a scientific master's thesis and present them convincingly to the department.

• Education: Master's degree in Business Informatics, Industrial Engineering, Business Administration or Economics with very good academic performance
• Experience and know-how:
  • Practical experience in data analysis with Python (especially Pandas, NumPy, Matplotlib/Seaborn)
  • First practical experience in data analysis, ideally gained through internships, working student positions or challenging university projects.
  • Basic knowledge of SQL
  • Ideally, initial experience in handling large data sets.
  • Experience with machine learning libraries (e.g., Scikit-learn) and knowledge of a dashboarding tool such as Power BI or Tableau is advantageous.
  • Demonstrable experience in handling unstructured or "unclean" datasets; initial insights into supply chain management, logistics, or risk management are desirable.
  • Ideally, initial experience in handling large data sets.
• Personality and work style: independent, structured and proactive person with analytical skills
• Daily work routine: your on-site presence is required
• Enthusiasm: strong interest in the topics of supply chain management, logistics and corporate security
• Languages: fluent German and English

For data protection reasons, applications are only accepted via our application portal. We regret that we cannot consider applications submitted by email or post.