8 beste vacatures voor de functie van data scientist in waterloo (nieuwe medewerkers gezocht)

Data Analyst L5 to L10 - Deadline 03/09/26
AlmavivA de Belgique —Brussel
- Voltijds
- Databases
- SQL
- Bachelor
Snel solliciteren
1 dag
Consultant Data & Analytics SAP - Datasphere / BDC l Belgium
Talan —Brussel
- Voltijds
- Computerwetenschappen
- Azure
- Informatica
Snel solliciteren
Data Analytics Engineer
Elmos —Brussel
- Azure
- SQL
12 dagen
Tribe Manager Microsoft - Data & Analytics
Devoteam —Machelen
- Voltijds
- Hybride werken
- Azure
- Management
- AWS
Data Pipeline Analyst – Senior
Mielabelo —Brussel
- Hybride werken
- Azure
- Oracle
- Engels
6 dagen
Enterprise Architect (Data and Analytics)
ING —Brussel
- Voltijds
- Azure
- Master
- ICT
Tribe Manager Microsoft - Data & Analytics
Devoteam Belgium —Machelen
- Voltijds
- Hybride werken
- Azure
- Management
- AWS
Snel solliciteren
Senior DATA Analyst H/F
Smile Group —Sint-Pieters-Woluwe
- Hybride werken
- Microsoft Office
- Analytisch vermogen
- Projectmanagement
Snel solliciteren

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Power BI Data Analyst | Mechelen Hybride
De Lijn —Mechelen
Bij Data & Analytics architectuur zorgen we met een team van een vijfentwintigtal mensen voor de uitbouw en het beheer van alle applicaties, technologieën en…
Data Product Analyst
Datashift —Leuven
First experience in data management: data catalog mgt, data modelling, data quality assessments, source to target analysis, data contracts, etc.;

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Data Analyst L5 to L10 - Deadline 03/09/26

AlmavivA de Belgique -
Brussel

Snel solliciteren

Vacaturegegevens

Voltijds
1 dag geleden

Kwalificaties

Power BI
Kubernetes
Spark
NumPy
Databases
SQL
Pandas
Docker
Bachelor
Machine learning
ETL
JSON
Python

Volledige vacaturetekst

Data extraction, mapping and integration: Extract, map, transform and integrate data from a range of large and very large data sources available within TAXUD, including, among others, Surveillance, TARIC, EORI, EBTI and ICS2, as well as relevant European Commission and external sources such as COMEXT and COMTRADE. Make the resulting data available in the appropriate analytical environments for further analysis and modelling.
Data processing and preparation: Design and implement efficient data-processing solutions for large volumes of data, taking into account the technical constraints and specific characteristics of the available analytical architectures and environments, including DataLab, ICS2/SSA and Power BI SNC.
Analytical visualisation and reporting: Create analytical products, reports, dashboards and clear and informative visualizations to communicate complex data findings to stakeholders using the available platforms and technologies, including Power BI SNC, Dataiku and Python, with a focus on supporting the analytical needs of the relevant stakeholders.

Integration and deployment of analytical solutions: Provide technical support for the deployment, integration, orchestration and configuration of analytical models and solutions within SSA, PowerBI SNC or temporary local solutions. Support the integration of such solutions into the relevant analytical workflows and environments.

Data Analysis and Modelling: Conduct exploratory data analysis, build predictive models, and extract insights from large datasets; Develop and implement machine learning algorithms to optimize customs risk assessment; Design or participate in the design of data processing algorithms for specific use cases.
Technical/User documentation: Produce and maintain comprehensive technical documentation covering the procedures for accessing, querying, extracting, transforming and making data available for analytical purposes. Documentation shall be structured and sufficiently detailed to enable peer review, knowledge transfer, maintenance and reproducibility by other members of the team, thereby ensuring continuity of the services.
Analytics, architecture and governance optimisation: Identify and propose improvements to analytical solutions, data architectures, data governance arrangements and related business processes. Proposals shall take into account the need to support multidisciplinary collaboration between Member States' customs experts and the Commission, within the applicable legal, security and governance framework, and shall cover both permanent and ad-hoc analytical use cases.
Machine learning and analytical experimentation: Support experimentation and further development of analytical capabilities, within the SSA platform where relevant, including the investigation, testing and evaluation of new approaches, machine-learning and deep-learning techniques, data-science methodologies and relevant software libraries and technologies, in cooperation with Member States where appropriate.
Other activities: Other tasks assigned by the direct manager

KNOWLEDGE AND SKILLS:

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)

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data scientist vacatures in waterloo

Data Analyst L5 to L10 - Deadline 03/09/26

Consultant Data & Analytics SAP - Datasphere / BDC l Belgium

Data Analytics Engineer

Tribe Manager Microsoft - Data & Analytics

Data Pipeline Analyst – Senior

Enterprise Architect (Data and Analytics)

Tribe Manager Microsoft - Data & Analytics

Senior DATA Analyst H/F

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