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

Status
Open
Remote policy
Not stated
Employment type
Part-time
Salary
Not stated
Tech
pythondatajunior
Source
arbeitnow
First observed
2026-09-09 15:30 UTC
Last seen
2026-09-09 15:30 UTC
Source claims posted
2026-09-09 14:09 UTC
Consecutive misses
0 of 3

What the posting says

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 ().

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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Quality

Completeness: 45%

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Timeline

  1. *
    #657607 2026-09-09 15:30 UTC
    Published