Graduate Student Research Assistant, Team Assessing Survey Data Quality (DE/EN) (SHK_SDM_2026_024)

Descripción del puesto

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, Politi