About the role
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.
Your tasks will be: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
Your profile: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
Our Benefits: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
ContactFor further information concerning the tasks, please contact Dr. Leah von der Heyde via E-Mail ().
Interested?Please apply via our online application portal. Applications will be reviewed on a rolling basis, so please don’t hesitate to apply!
Our reference number is: SHK_SDM_2026_024
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