Senior Data Scientist – Belgium (Hybrid)

Hive-X; Brussel Hoofdstad; Sur place; EUR 70 000 - 90 000; Temps partiel

Résumé du poste

Hive-X is seeking a Senior Data Scientist to lead strategic data initiatives in Brussels (Hybrid). This role involves technical leadership in advanced analytics, coaching team members, and managing ad-hoc data requests.

The ideal candidate will have a Master's in IT, strong experience with Python and ML models, and be trilingual (NL/FR/EN). The contract type is freelance, starting June 22, 2026, for a duration of 7 months.

Qualifications

• Strong, hands-on experience as a Data Scientist/ML Engineer with a focus on Python.
• Experience with data analysis and modelling (pandas, scikit-learn) and building/improving ML models in production.
• Trilingualism (NL/FR/EN) strongly desired.
• Strong software engineering foundation: Git, CI/CD pipelines, Docker.

Responsabilités

• Take on technical lead in complex data processes.
• Coach and guide data scientists through co-creation.
• Create an overview of incoming questions and manage project follow-ups.
• Close collaboration with data engineers and stakeholders.

Connaissances

Python

Data analysis

Data modelling

Agile / Scrum

ML model building

SQL

Docker

DevOps (CI/CD)

Git

Terraform

Formation

Master in IT

Outils

Pandas

scikit-learn

AWS/GCP

FastAPI

Databricks

Description du poste

Senior Data Scientist – Belgium (Hybrid)

About the Mission Context & purpose of the role: The Data mining team is looking for a senior profile who fulfills a dual assignment: Accelerating strategic data initiatives through substantive expertise, technical direction and coaching. Manage the continuous flow of ad‑hoc data questions through overview, prioritization, bundling and translation into reusable solutions/data products. The role brings seniority, structure and technical depth to the Data mining team, while supporting the operation and follow-up together with the team manager and other stakeholders.

Core responsibilities: 1) Strategic projects & technical leadership: Take on the technical lead in complex data processes (e.g. advanced analytics, graph/network analytics, integrations, architectural choices). Help shape the approach, solutions and priorities of larger initiatives, with an eye for feasibility, impact and scalability. Monitor and promote quality standards, including reproducibility, documentation, methodology and – where relevant – engineering quality. 2) Team uplift & co‑creation (within Data mining): Coach and guide data scientists and analysts through co‑creation, substantive reviews and sharing best practices. Contribute structurally to increasing team competencies (methodology, approach, quality, communication). Take an active role in developing team agreements, such as definition of done, working methods and knowledge sharing. 3) Structuring and productising ad‑hoc demand flow: Create an overview of incoming questions: intake, slicing, prioritisation, status/communication. Cluster ad‑hoc work and, where possible, convert it to structural, reusable solutions (reusable datasets, analysis methods, templates, data products). Apply FAIR principles from a data product point of view with a focus on reusability and quality. 4) Project management & follow‑up (stretch): Include basic delivery/project follow‑up (scope, milestones, dependencies, risks). Support the team leader in follow‑up and coordination to bring stability to planning and implementation. Contribute to stakeholder alignment, including expectation management, decision‑making and (where necessary) escalations.

Collaboration & stakeholders: Close collaboration within the Data mining team (data scientists/analysts, and where relevant data engineers/platform stakeholders). Collaboration with Data Platform team and substantive partners/stakeholders. Work in an environment with multiple priorities, where there is a need for structure in intake, follow‑up and communication.

Profile (must‑haves): Master in IT Strong, hands‑on experience as a Data Scientist / ML Engineer with a focus on Python. Experience with data analysis and modelling (pandas, scikit‑learn) and building/improving ML models in a production context. Strong software engineering foundation: Git, code reviews, CI/CD pipelines, Docker; experience setting up APIs and reusable components (e.g. FastAPI). Knowledge of SQL; experience with infrastructure‑as‑code or cloud is a plus (Terraform, AWS/GCP). Strong in structuring unclear questions and translating them into concrete approaches/deliveries. Experience with coaching/mentoring and working in co‑creation (e.g. technical training, reviews, SCRUM/scrum master role). Strong communication skills (involving stakeholders, reporting clearly, managing expectations). Trilingualism (NL/FR/EN) strongly desired and preferably at a high level.

Positives (nice‑to‑haves): Experience with data product thinking, governance and quality principles (FAIR, definitions, documentation, reusability). Experience with graph analytics / network analytics or other advanced analytics domains. Knowledge of Databricks. Previous experience within an OISZ is a big plus. Previous experience with secondary data use and fraud detection.

Required Skills: Agile / Scrum, AI & Machine learning, API, AWS, CI/CD pipelines, Data analysis, Data modelling, Docker, GCP, GIT, Java, ML‑modellen, Pandas, Python, SQL, Terraform

Practical Information

• Company: Confidential
• Location: Bruxelles – Hybrid
• Start Date: 22 June 2026
• End Date: 31 December 2026
• Duration: 7 months
• Contract Type: Freelance / Mission
• Application Deadline: 16 June 2026