5+ years of professional experience as a Data Scientist, with expert-level mastery of the Python data ecosystem (Pandas, NumPy, SciPy, Scikit-Learn)
Deep theoretical and practical knowledge of supervised and unsupervised learning, regression, classification, clustering, and time-series forecasting
Proven track record of moving models out of Jupyter Notebooks and into production environments using containerization (Docker) and microservice design
Strong proficiency in writing complex, optimized SQL queries and experience handling large-scale data using distributed computing frameworks like PySpark or Ray
Experience with machine learning lifecycle tools (such as MLflow, DVC, or Weights & Biases) for model tracking, versioning, and feature store management
Hands-on experience leveraging cloud data infrastructure (AWS, GCP, or Azure) and managed ML services (e.g., SageMaker or Vertex AI)
Time zone: CET (+/- 3 hours). We are unable to consider applications from candidates in other time zones
Deep Learning experience using PyTorch or TensorFlow/Keras
Experience with NLP frameworks (Hugging Face, spaCy) or deploying LLM-based pipelines (LangChain, vector databases)
Familiarity with orchestration tools like Apache Airflow or Prefect
Strong background in experimental design, A/B testing methodologies, and statistical significance validation