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

<br><strong> </strong><p><span style="font-family:Tahoma, sans-serif;">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.</span><br><span style="font-family:Tahoma, sans-serif;"> </span><br><span style="font-family:Tahoma, sans-serif;">Starting <strong>as soon as possible,</strong> our Department <strong>Survey Design &amp; Methodology (SDM)</strong>, <strong>Team Assessing Survey Data Quality,</strong> located in <strong>Mannheim</strong>, is looking for a</span><br><span style="font-family:Tahoma, sans-serif;"> </span><br><strong><span style="font-size:16px;font-family:Tahoma, sans-serif;">Graduate Student Research Assistant</span></strong><br><strong><span style="font-size:16px;font-family:Tahoma, sans-serif;">(16,09 € hourly rate, 40 hrs./ month, temporary)</span></strong><br><span style="font-family:Tahoma, sans-serif;"> </span><br><p style="line-height:115%;"><span style="line-height:115%;font-family:Tahoma, sans-serif;">The department </span><a href="https://www.gesis.org/en/institute/departments/survey-design-and-methodology/"><strong><span style="line-height:115%;font-family:Tahoma, sans-serif;color:#0070C0;">Survey Design &amp; Methodology (SDM)</span></strong></a><span style="line-height:115%;font-family:Tahoma, sans-serif;"> 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</span><span style="line-height:115%;font-family:Tahoma, sans-serif;color:#0070C0;"> </span><a href="https://www.gesis.org/en/institute/about-us/staff/orga/tiles/6/88?cHash=bed568c098864cd48086ba9153d59c32"><strong><span style="line-height:115%;font-family:Tahoma, sans-serif;color:#0070C0;">Assessing Survey Data Quality (ASDQ)</span></strong></a><span style="line-height:115%;font-family:Tahoma, sans-serif;"> 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.</span></p></p><br><strong>Your tasks will be:</strong><p><ul style="list-style-type:disc;margin-left:15.05px;"><li>Co<span style="line-height:150%;font-family:Tahoma, sans-serif;font-size:15px;">-development of a data quality analysis pipeline in R/Python, combining packages and developing new workflows</span></li><li><span style="line-height:150%;font-family:Tahoma, sans-serif;font-size:15px;">Collection, processing, and analysis of (AI-generated) survey data</span></li><li><span style="line-height:150%;font-family:Tahoma, sans-serif;font-size:15px;">Literature / software research on silicon sampling evaluation and data quality evaluation metrics</span></li><li><span style="line-height:150%;font-family:Tahoma, sans-serif;font-size:15px;">Preparation of documentation and presentations</span></li><li><span style="line-height:150%;font-family:Tahoma, sans-serif;font-size:15px;">Administrative support for AI-related survey research and services</span></li></ul></p><br><strong>Your profile:</strong><p><ul style="list-style-type:disc;margin-left:15.05px;"><li><span style="line-height:150%;font-family:Tahoma, sans-serif;font-size:15px;">Strong programming skills (in R; experience with Python is a plus)</span></li><li><span style="line-height:150%;font-family:Tahoma, sans-serif;font-size:15px;">Good knowledge of statistical concepts and methods (theory and practice)</span></li><li><span style="line-height:150%;font-family:Tahoma, sans-serif;font-size:15px;">Experience in processing and analyzing social science data</span></li><li><span style="line-height:150%;font-family:Tahoma, sans-serif;font-size:15px;">Reliable, rigorous, and independent in problem-solving</span></li><li><span style="line-height:150%;font-family:Tahoma, sans-serif;font-size:15px;">Proficient in English (good knowledge of German is a plus)</span></li><li><span style="font-family:Tahoma, sans-serif;font-size:15px;">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</span></li></ul></p><br><strong>Our Benefits:</strong><p><ul style="list-style-type:disc;"><li><span style="font-family:Tahoma, sans-serif;color:#000000;">Working on a cutting-edge and relevant topic at the intersection of survey data quality and AI</span></li><li><span style="font-family:Tahoma, sans-serif;color:#000000;">Very good conditions for reconciling work and family life</span></li><li><span style="font-family:Tahoma, sans-serif;">Flexible working hours and regulations for mobile working</span></li><li><span style="font-family:Tahoma, sans-serif;color:#000000;">Holistic company health management and discounted participation in the university's sports programme </span></li><li><span style="font-family:Tahoma, sans-serif;color:#000000;">Promotion of your skills through further training measures through GESIS Training</span></li></ul></p><br><strong>Contact</strong><p><span style="font-family:Tahoma, sans-serif;">For further information concerning the tasks, please contact Dr. Leah von der Heyde via E-Mail (</span><a ----- style="font-family:Tahoma, ----- style="font-family:Tahoma, sans-serif;">).  If you have questions about the application process, please contact Michaela Kurtov via E-Mail (</span><a ----- style="font-family:Tahoma, ----- style="font-family:Tahoma, sans-serif;">).</span></p><br><strong>Interested?</strong><p><span style="font-family:Tahoma, sans-serif;">Please apply via our online application portal. Applications will be reviewed on a <strong>rolling basis</strong>, so please don’t hesitate to apply!</span><br><span style="font-family:Tahoma, sans-serif;">Our reference number is: </span><strong><span style="font-family:Tahoma, sans-serif;">SHK_SDM_2026_024</span></strong></p><p>Find <a href="https://www.arbeitnow.com">Jobs in Germany</a> on Arbeitnow</a>