Are you passionate about turning complex data into intelligent, production-ready solutions while supporting the future of open-access science? We are looking for a Data Scientist to join our team and help design, develop, and deploy advanced machine learning solutions that power intelligent, data-driven products. This role focuses on NLP, recommender systems, and agentic LLM workflows, turning complex business challenges into scalable and impactful analytical solutions.
This is an opportunity to work at the forefront of machine learning and AI, contributing to innovative systems that leverage state-of-the-art research in practical applications.
As part of our commitment to fostering a collaborative team environment, this role requires working fully on-site.
Core Responsibilities
Design, develop, and evaluate machine learning and statistical models, with a focus on NLP, recommender systems, and LLM-based solutions.
Translate business problems into data-driven approaches and scalable analytical solutions.
Perform data exploration, preprocessing, and feature engineering to ensure high-quality inputs for modeling.
Develop, optimize, and benchmark models using appropriate metrics and validation strategies.
Build and experiment with LLM-powered systems, including agentic workflows and RAG.
Investigate, reproduce, and compare research prototypes and state-of-the-art methods.
Collaborate with engineering teams to support the integration of models into production.
Monitor and analyze model performance and iterate to improve accuracy and robustness.
Additional Responsibilities
Supervising Master/ Ph.D. students when there are trainees in the team.
Representing the company in e.g. attending conferences, writing scientific articles.
Requirements
Bachelor’s degree/ Master's degree in Computer Science or related.
2-5 years of experience as a Data Scientist.
2–5 years of experience in Python (including complex applications), Machine Learning, and LLMs.
Strong background in data science, statistics, and analytical problem-solving.
Intermediate proficiency in FastAPI, Celery, and Keycloak.
Intermediate proficiency in PyTorch, TensorFlow, Scikit-learn, and Hugging Face.
Proficiency in Natural Language Processing (NLP), including tokenization and named entity recognition (NER).
In-depth understanding of Artificial Intelligence principles.
Strong working knowledge of Microsoft O365 tools.
Excellent written and spoken English.
Excellent communication skills, capable of conveying complex technical concepts to non-technical stakeholders.
Ability to work effectively both independently and as part of a team.
Nice to have
What we offer
The opportunity to contribute to the academic/scientific community;
Flexible working hours;
Team bond strengthening through team-building events;
Professional growth opportunities with our global training system;
Working in a collaborative and socially responsible team;
Company retreat facility;
Full-coverage insurance for accidents/daily sickness;
Prime location near Basel train station and city center;
And more.
If you are interested in this position, we look forward to receiving:
A motivational letter (EN) that briefly describes your motivations for joining MDPI;
A resume (EN) including personal information, past & current education;
Copy of ID and Diplomas;
If available: reference letters and certificates.
About MDPI
Headquartered in Switzerland, MDPI is a fully Open Access publisher with a portfolio of more than 500 journals across all scientific disciplines. To date, MDPI has published the works of over 4.5 million researchers, collaborating with an extensive network of academic institutions and scientific societies worldwide. Above all, MDPI is committed to ensuring that high-quality research is freely accessible to readers across the globe.
Initiatives
At MDPI, we develop and maintain various platforms in order to better serve the scientific community. Please find here-below a list of our main platforms:
https://www.mdpi.com
https://www.mdpi.com/books/
https://sciprofiles.com
https://sciforum.net
https://www.scilit.net
https://www.preprints.org
https://encyclopedia.pub
#LI-Onsite
