Basel
Yesterday
Internship in Applied NLP
- 19 February 2026
- 100%
- Permanent position
- Basel
Job summary
Join BAK Economics, a leading research company in Basel. Exciting internship in NLP awaits!
Tasks
- Engage in real-world NLP projects and data preparation.
- Analyze texts for regulation monitoring and labor market insights.
- Collaborate closely with the Modelling & AI team in Basel.
Skills
- Seeking master's students with NLP coursework or project experience.
- Proficiency in NLP and data analysis techniques required.
- B1 level German proficiency is essential for text processing.
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DE (English version below)
BAK Economics is a renowned economic research company based in Basel. In our more than 45-year history, we have built extensive expertise in economic analysis and forecasting for all sectors of the Swiss economy and enjoy a high reputation in this field.
The field of artificial intelligence, and in particular natural language processing, offers diverse opportunities to expand economic insights and provide action-oriented recommendations.
We offer a full-time internship position in applied natural language processing for master’s students with course or project experience in NLP. You will work on real challenges and cover the entire process from data preparation to model evaluation. Two core application areas are regulation monitoring and labor market analysis, where we process a range of texts (laws, job offers, etc.) to track developments and gain comprehensive insights into these important aspects.
Our ideal candidate has the following skills:
The internship lasts at least 6 months, during which you spend 2-3 days per week in our Basel office to work closely with our Modelling & AI team. You will gain practical experience applying NLP to real economic questions and be supervised by experienced experts.
The start date is flexible, earliest in April 2026. The working language can be either English or German. However, since some of the source texts are written in German, applicants should have at least B1 level German skills.
If you are interested, please send your application to jobs(at)bak-economics.com. Send us your CV, transcript, a motivation letter, and ideally examples of your previous work (e.g., a GitHub repo). We review applications on a rolling basis.
EN
BAK economics is a well-established economics research company based in Basel, Switzerland. Over its history of over 45 years, we have built up a broad competence of economic analysis and forecasting in all sectors of the Swiss economy and are a well-reputed voice in the field.
The field of artificial intelligence, and natural language processing in particular, offers a wide range of possibilities to extend economic insights and provide action-oriented recommendations.
We are offering a fulltime internship position in applied natural language processing for master’s-level students with coursework or project experience in NLP. You will work on real-world challenges and cover the entire pipeline from data preparation to model evaluation. Two core application areas are regulation monitoring and labor market analysis, where we process a range of texts (laws, job offers, etc.) to track developments and offer comprehensive insights into these important aspects.
Our ideal candidate has the following skills:
Internships are for a duration of 6 months or more, with 2-3 days per week in our Basel office to allow for a close collaboration with our Modelling & AI team. You'll gain hands-on experience applying NLP to real economic questions, with mentorship from experienced practitioners.
The start date is flexible, earliest in April 2026. The working language can be either English or German. However, as some of the source texts are in German, participants should have at least a B1 level of German proficiency.
If this sounds interesting to you, please reach out to jobs(at)bak-economics.com. Send us your CV, transcript of records, a statement of your motivation, and, ideally, examples of prior work (e.g., a github repo). We are reviewing applications on a rolling basis.