Senior Data Scientist (Python)

The Role:

We are looking for a Senior Data Scientist (Python) to join one of our high-growth client teams as a technical lead in data innovation. In this role, you will be responsible for transforming complex, high-velocity datasets into predictive models and actionable systems that directly influence product strategy and business operations.

You will bridge the gap between pure research and production software engineering. You will not just build prototypes in notebooks; you will design, train, evaluate, and deploy robust machine learning models into live cloud environments. This is a role for a systems-thinking data scientist who values clean, modular Python architecture, statistical rigor, and scalable data pipelines.

What we are looking for:

• 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.

Nice-to-have:

• 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.

Responsibilities:

• Design, build, and optimize scalable predictive models and machine learning algorithms to solve complex business challenges.
• Architect and maintain robust data pipelines and feature sets, ensuring data quality, consistency, and integrity across training and inference layers.
• Partner with Data Engineers, Product Managers, and Backend Teams to integrate ML models seamlessly into core application APIs.
• Conduct rigorous peer code reviews for data science workflows, championing production-grade Python design patterns and linting standards.
• Translate highly technical metrics (Precision, Recall, ROC-AUC, RMSE) into clear business outcomes and executive-level recommendations.

What we offer:

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