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

Meyandy LLC; Mannheim; Vor Ort; EUR 18.000 - 26.000; Vollzeit

Benefits dieser Stelle

Flexible working hours

Mobile working

Discount for university sports program

Training opportunities

Zusammenfassung

GESIS – Leibniz-Institute for the Social Sciences in Mannheim seeks a Graduate Student Research Assistant to assist in evaluating synthetic data quality. You will help build a data quality pipeline in R/Python, process AI-generated survey data, and conduct literature research while preparing documentation and presentations.

This is a temporary, part-time role aligned with a Master’s program and offers flexible options.

Qualifikationen

• Proficient in R programming and able to use Python as a plus.
• Strong knowledge of statistical concepts and methods.
• Experience in processing and analyzing social science data.
• Enrolled in a Master’s program at the intersection of social and data science with a quantitative focus.

Aufgaben

• 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.

Kenntnisse

R

Python

Statistics

Social data analysis

Ausbildung

Master's student in Social/Data Science

Tools

R

Python

Jobbeschreibung

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

Contact For further information concerning the tasks, please contact Dr. Leah von der Heyde via E-Mail (). If you have questions about the application process, please contact Michaela Kurtov via E-Mail ().

Our reference number is: SHK_SDM_2026_024