We are looking for a motivated Working Student Fraud Data Engineering / Analytics to support our Fraud Engineering team. You will work with fraud-related data, support fraud monitoring and investigation processes, and contribute to dashboards, reporting, and scalable fraud detection logic across modern e-commerce operations. This role combines Fraud Analytics, Data Engineering, SQL, Python, and Business Intelligence and offers hands-on experience in building data-driven fraud prevention solutions without impacting customer experience.
Your Responsibilities
Support the Fraud Engineering team with data analysis, fraud monitoring, and fraud investigation across e-commerce processes
Prepare, structure, and analyze large datasets using SQL, Python, and analytics tools
Contribute to the development of fraud detection logic, dashboards, reporting solutions, and internal fraud analytics tools
Help identify fraud patterns, suspicious behavior, account abuse, payment fraud, and operational anomalies through data-driven insights
Collaborate closely with cross-functional teams to improve scalable fraud prevention processes and data quality standards
Your Profile
Ongoing Master’s studies in Data Science, Data Engineering, Computer Science, Business Informatics, Statistics, Mathematics, or a comparable technical field
Good practical knowledge of SQL and experience working with structured datasets and analytical workflows
Basic experience with Python for scripting, automation, or data analysis
Strong analytical mindset with interest in Fraud Detection, Risk Analytics, Payment Fraud, Account Abuse, and E-Commerce
Structured and hands-on working style with the ability to work independently while collaborating closely with the team
#LI-MT1; #LI-HybridMunich; #LI-HybridBerlin
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