
Data Scientist
- Madrid
- Permanente
- Tiempo completo
- Analyze time series data and perform data quality control for wind, solar, and storage technologies.
- Apply statistical and machine learning techniques to uncover insights from complex datasets.
- Collaborate with cross-functional teams to integrate analytical outputs into operational workflows.
- Train and evaluate models with effective hyperparameter tuning and result validation to support business decisions.
- Use Kubernetes to manage and deploy machine learning workflows in containerized environments.
- Work with databases such as MongoDB, ClickHouse, and PostgreSQL, for data storage and processing.
- Degree in Engineering, Mathematics, or Computer Science.
- Strong proficiency in Python, including libraries such as NumPy, Pandas and Scikit-learn.
- Solid foundation in applied statistics, including the series analysis, clustering, distribution analysis, and hypothesis testing.
- Practical experience with machine learning algorithms, especially clustering, regression and boosting methods (e.g. XGBoost, LightGBM, CatBoost).
- Experience in hyperparameter tuning and model performance optimization.
- Hands-on experience with time series modeling and analysis.
- Working knowledge of databases such as MongoDB, ClickHouse, or PostgreSQL.
- Familiarity with AutoML tools to streamline model development.
- Basic Knowledge of Kuberflow for orchestrating machine learning workflows.
- Experience with cloud platforms (Kubernetes, AWS, Azure) and containerization tools like Docker is a plus.
- Prior experience int he renewable energy domain is a strong advantage.
- Deep learning techniques and frameworks such as TensorFlow or PyTorch is a plus.