GESIS – Leibniz-Institute for the Social Sciences is an internationally active research institute, funded by federal and state governments and member of the Leibniz Association.
 
Starting as soon as possible, our Department Survey Design & Methodology (SDM), Team Assessing Survey Data Quality, located in Mannheim, is looking for a
 
Graduate Student Research Assistant
(16,09 € hourly rate, 40 hrs./ month, temporary)
 

The department Survey Design & Methodology (SDM) is both nationally and internationally recognized for its expertise in survey methodology, gained over many years by conducting own research as well as consulting on and implementing renowned survey projects. The team Assessing Survey Data Quality (ASDQ) advises researchers on the data quality of survey and ancillary data and develops indicators, guidelines, tools, and training materials for social scientists. In this role, you will assist the project on Evaluating Synthetic Data Quality (SYNQ), building a programmatic pipeline for detecting quality issues in AI-generated survey data.

• Co-development of a data quality analysis pipeline in R/Python, combining packages and developing new workflows
• Collection, processing, and analysis of (AI-generated) survey data
• Literature / software research on silicon sampling evaluation and data quality evaluation metrics
• Preparation of documentation and presentations
• Administrative support for AI-related survey research and services

• Strong programming skills (in R; experience with Python is a plus)
• Good knowledge of statistical concepts and methods (theory and practice)
• Experience in processing and analyzing social science data
• Reliable, rigorous, and independent in problem-solving
• Proficient in English (good knowledge of German is a plus)
• Enrolled in a Master’s program at the intersection of social and data science, e.g., (Social) Data Science, Computational (Social) Science, Big Data, or Social Sciences (e.g., Sociology, Political Science) with a quantitative focus

• Working on a cutting-edge and relevant topic at the intersection of survey data quality and AI
• Very good conditions for reconciling work and family life
• Flexible working hours and regulations for mobile working
• Holistic company health management and discounted participation in the university's sports programme 
• Promotion of your skills through further training measures through GESIS Training

For further information concerning the tasks, please contact Dr. Leah von der Heyde via E-Mail ().

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