Senior Data Scientist

MC Expert

Brussels, Belgiumhybridcontractsenior

We are looking for a Senior Data Scientist to accelerate strategic data initiatives and manage ad-hoc data requests. The role involves technical leadership, team coaching, and project management within the Data mining team, requiring strong experience in Python and data analysis.

Description (English)

Context & Purpose of the Role:

The Data mining team is looking for a senior profile to fulfill a dual role:
Accelerate strategic data initiatives through subject matter expertise, technical direction, and coaching.
Manage the continuous stream of ad-hoc data requests by overseeing, prioritizing, bundling, and translating into reusable solutions/data products.
The role brings seniority, structure, and technical depth to the Data mining team while also supporting operations and follow-up alongside the team leader and other stakeholders.

Key Responsibilities:

1) Strategic Projects & Technical Leadership

•Take on the technical lead in complex data trajectories (e.g., advanced analytics, graph/network analytics, integrations, architectural choices).

•Help shape the approach, solutioning, and priorities of larger initiatives, considering 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 Productizing Ad-Hoc Requests

•Create an overview of incoming requests: intake, slicing, prioritization, status/communication.

•Cluster ad-hoc work and, where possible, convert it into structural, reusable solutions (reusable datasets, analysis methods, templates, data products).

•Apply FAIR principles from a data product standpoint with a focus on reusability and quality.

4) Project Management & Follow-Up (Stretch)

•Take on basic delivery/project follow-up (scope, milestones, dependencies, risks).

•Support the team leader in follow-up and coordination to bring stability to planning and execution.

•Contribute to stakeholder alignment, including expectation management, decision-making, and (where necessary) escalations.

Collaboration & Stakeholders:

•Work closely within the Data mining team (data scientists/analysts, and where relevant data engineers/platform stakeholders).

•Collaborate with the Data Platform team and substantive partners/stakeholders.

•Work in an environment with multiple priorities, where structure is needed in intake, follow-up, and communication.

Profile (Must-Haves):

•Master's in IT.

•Strong, hands-on experience as a Data Scientist / ML Engineer with a focus on Python.

•Experience with data analysis and modeling (pandas, scikit-learn) and building/improving ML models in a production context.

•Strong software engineering foundation: Git, code reviews, CI/CD pipelines, Docker; experience with 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/deliverables.

•Experience with coaching/mentoring and working in co-creation (e.g., technical training, reviews, SCRUM/scrum master role).

•Strong communication skills (engaging stakeholders, clear reporting, managing expectations).

•Trilingual (NL/FR/EN) strongly desired and preferably at a high level.

Plus Points (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 significant plus.

•Previous experience with secondary data use and fraud detection.

Expected Impact (3-6 months):

•Clearer intake and prioritization process for ad-hoc requests to the Data mining team.

•More reusable and scalable outputs instead of one-offs.

•Measurable uplift in team quality through coaching, reviews, and methodological agreements.

•Better predictability and progress on important data trajectories and strategic initiatives.

Along with your CV, we ask you to submit the result of the exercise below. Failure to submit a response or if the answers do not meet the requirements will result in the candidate not being retained: explain how a random forest works and in which situations you would prefer XGBoost or AdaBoost compared to a Random Forest.

Competitive

Job Info

Type

contract

Level

senior

Work

hybrid

Salary

Competitive

Posted

Jun 9, 2026

Start Date

Jun 22, 2026

Deadline

Jun 16, 2026

Tech Stack

PythonPandasScikit-learnGitDockerCI/CD pipelinesTerraformAWSGCPFastAPI