Data Scientist – NLP, Recommender Systems & LLMs

About the role

We are seeking a passionate Data Scientist to design, develop, and deploy advanced machine learning solutions that power intelligent, data‑driven products. The focus will be on natural language processing, recommender systems, and agentic LLM workflows, turning complex business challenges into scalable analytical solutions.

Key responsibilities

• Design, develop, and evaluate ML and statistical models, especially in NLP, recommender systems, and LLM‑based solutions.
• Translate business problems into data‑driven approaches and scalable analytical pipelines.
• Perform data exploration, preprocessing, and feature engineering to ensure high‑quality model inputs.
• Develop, optimize, and benchmark models using appropriate metrics and validation strategies.
• Build and experiment with LLM‑powered systems, including agentic workflows and retrieval‑augmented generation (RAG).
• Investigate, reproduce, and compare research prototypes and state‑of‑the‑art methods.
• Collaborate with engineering teams to integrate models into production and monitor performance.
• Supervise Master/PhD students and represent the company at conferences and in scientific publications.

Required profile

• Bachelor’s or Master’s degree in Computer Science or a related field.
• 2–5 years of experience as a Data Scientist.
• Strong background in data science, statistics, and analytical problem‑solving.
• Excellent written and spoken English and strong communication skills.
• Ability to work independently and as part of a collaborative team.
• Nice to have: PhD in Computer Science or related; leadership experience mentoring junior engineers.

Required skills

• Python (including complex applications).
• FastAPI, Celery, Keycloak.
• PyTorch, TensorFlow, Scikit‑learn, Hugging Face.
• Natural Language Processing (tokenization, named entity recognition).
• Experience with Large Language Models (LLMs) and agentic workflows.
• Proficiency with Microsoft O365 tools.

What we offer

• Opportunity to contribute to the academic and scientific community.
• Flexible working hours.
• Team‑building events and a collaborative, socially responsible environment.
• Professional growth through a global training system.