Student Assistant

The Leibniz Institute SAFE is seeking to fill the position of a
Student Assistant (M/F/d) in the Area of Finance and Data Science
Timeframe
40 hours / month
Fixed-Term
max. 12 months
Payment
based on study progress
Start
at the earliest possible date
The Leibniz Institute for Financial Research SAFE - Sustainable Architecture for Finance in Europe - ) promotes scientific research, transfer work to the public and independent policy advice on all aspects relating to the structure and functioning of the financial system. It aims to contribute to strengthening a sustainable and resilient financial system that drives innovation and serves the needs of the economy and citizens. Researchers from the fields of economics, law, and political science collaborate at SAFE.
**Your tasks**:
Your tasks depend on the project you will be working on. There are two different types of positions.
**Project 1**: “SAFE Manager Sentiment Index”
In January 2025, SAFE has started releasing its Manager Sentiment Index (). This new monthly economic indicator captures top management’s sentiment from earnings conference call transcripts and financial statements using textual analysis methods.
You will join SAFE’s team working on the manager sentiment index. Besides completing the regular monthly index updates, the team also aims to develop the index further and cooperates with SAFE’s Policy Center on analyses around specific topics (e.g., tariffs and trade tensions). Your responsibilities will include assisting in the collection and pre-processing of the text data and supporting empirical analyses. Contributions to the Policy Center’s work around the index are also very welcome. This project offers the opportunity to acquire/improve your programming skills and to deepen your knowledge of data management. Furthermore, you will gain insights into how research-based insights are communicated to the broader public.
**Project 2**: “German historical financial database”
You will be involved in our historical research projects and will contribute to building a unique firm-level financial database from historical sources (e.g., Handbuch der Deutschen Aktiengesellschaft, Kursblatt der Berliner Börsen-Zeitung). You will support our project team, for example, in the data cleaning and validation process or in the generation of training data. On this job, you will have the opportunity to acquire or improve your programming skills and deepen your knowledge of data management. Moreover, you will gain insights into empirical research in economics and finance.
**Your profile**:

- According to - 6 WissZVG in conjunction with - 82 HHG, your enrolment at a German university is a mandatory requirement for employment at our institute
- Student of accounting, economics, finance, management, or a related discipline.
- High personal motivation and good self-management.
- Good English skills. For Project 1, no German language skills are needed. For Project 2, German language skills are welcome.
- Previous experience with an empirical project or empirical research (e.g., bachelor or seminar thesis) is a plus but not required. Programming skills are also very welcome, but are not required.

**We offer**:
We offer the infrastructure of a modern research center with excellent contacts to the scientific and financial community. You will be working in a very attractive working environment in the House of Finance at Goethe University with an enthusiastic team of colleagues. The position is for 12 months (a shorter duration is possible) with 40 working hours per month (fewer working hours per month are also possible); working days and times are flexible based on mutual agreement. To gain additional insights into the research conducted at SAFE, you can join the weekly SAFE brown bag seminar in which Ph.D. students, postdocs, and faculty present their research.
SAFE seeks to increase the share of women in research and thus strongly encourages female scholars to apply. We are also committed to promote the inclusion of persons with disabilities and therefore invites them in particular to apply.