- Data integration and processing: At least 3 years of professional experience in extracting, transforming, integrating and preparing data from multiple sources, including large or complex datasets, and in making data available for analytical use.
- Business Intelligence and visualisation: At least 3 years of professional experience in developing analytical reports, dashboards and data visualisations, in particular using Power BI or comparable Business Intelligence tools.
- Python and SQL: Strong practical knowledge of Python and SQL, with professional experience using these technologies for data extraction, transformation, analysis and preparation. At least 2 years of professional experience with commonly used Python data-science libraries, such as Pandas and NumPy, is required.
- ETL and data workflows: Practical experience in designing, developing and maintaining ETL and data-processing workflows, including data extraction, transformation, validation, quality checks and preparation for downstream analytical use.
- Databases and data management: Good knowledge of databases, data modelling, data wrangling and data management, including relational databases, SQL and analytical/data-warehouse concepts.
- Large-scale data processing: Practical understanding of the challenges associated with processing large volumes of data, including data volume, processing time, memory, storage and performance constraints. Experience with distributed or parallel processing technologies such as Spark is an asset.
- Data formats: Good knowledge of commonly used data formats, including JSON and Parquet. Knowledge of DuckDB is an asset.
- Analytical methods:



Good knowledge of data analysis, statistics and relevant analytical methods, with the ability to select and apply appropriate techniques to practical business and operational questions.
- Statistics and probability: Good knowledge of statistics and probability, including distributions, sampling techniques, hypothesis testing, statistical analysis and model evaluation metrics.
- Machine learning: Practical experience with machine-learning techniques and with the main stages of an ML project, including data preparation, exploratory analysis, model development, evaluation and, where relevant, deployment. Experience with Scikit-learn is an asset.
- Analytical environments and deployment: Good understanding of the technical considerations involved in making analytical solutions available in different environments. Knowledge of containerisation and orchestration technologies such as Docker and Kubernetes is an asset.
- Data quality, governance and protection: Good understanding of data quality, data governance, data security and data-protection principles relevant to analytical data processing.
- Problem-solving: Strong ability to investigate technical and analytical problems,



identify their root causes and develop practical and maintainable solutions, including when working with large or complex datasets.
- Documentation and communication: Ability to document data-processing procedures, analytical solutions and technical configurations clearly and to communicate effectively with both technical and non-technical stakeholders proven with at least 2 years of professional experience.
- Collaborative working: Ability to work effectively in multidisciplinary teams and to interact with data analysts, technical specialists and subject-matter experts.

SPECIFIC EXPERTISE:

- Analytical use cases: At least 2 years of professional experience translating Union customs or equivalent complex operational requirements into analytical use cases and communicating analytical results to subject-matter experts and other non-specialist stakeholders. Experience in Union customs risk analysis is an asset.
- Innovation and experimentation: Experience evaluating and testing new data-science, machinelearning or analytical technologies and assessing their practical applicability.
- Heterogeneous data sources: Demonstrated experience working with data originating from multiple heterogeneous sources and systems, including the integration and preparation of data for analytical use.
- Technical troubleshooting and delivery: Demonstrated ability to investigate and resolve technical issues affecting data extraction, processing, integration and analytical solutions, and to work effectively across different technical environments.

Level : 5 to 10

Delivery mode: Near Site (Brussels)

📌 Data scientist (España)
🏢 AlmavivA de Belgique
📍 España