Context & purpose of the role:

The Data mining team is looking for a senior profile to fulfill a dual mission:

• Accelerate strategic data initiatives through substantive expertise, technical direction, and coaching.
• Manage the continuous stream of ad-hoc data requests by providing oversight, prioritization, bundling, and translation 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 together with, among others, the team lead and other stakeholders.

Core responsibilities:

1) Strategic projects & technical leadership

• Taking on the technical lead in complex data projects (e.g., advanced analytics, graph/network analytics, integrations, architectural choices).
• Helping to shape the approach, solutioning, and priorities of larger initiatives, with an eye for feasibility, impact, and scalability.
• Safeguarding and promoting quality standards, including reproducibility, documentation, methodology, and – where relevant – engineering quality.

2) Team uplift & co-creation (within Data mining)

• Coaching and guiding data scientists and analysts through co-creation, substantive reviews, and sharing best practices.
• Structurally contributing to the enhancement of team competencies (methodology, approach, quality, communication).
• Taking an active role in developing team agreements, such as definition of done, working methods, and knowledge sharing.

3) Structuring and productizing the ad-hoc request stream

• Creating an overview of incoming requests: intake, slicing, prioritization, status/communication.
• Clustering ad-hoc work and, where possible, converting it into structural, reusable solutions (reusable datasets, analysis methods, templates, data products).
• Applying FAIR principles from a data product perspective with a focus on reusability and quality.

4) Project management & follow-up (Stretch)

• Taking on basic delivery/project follow-up (scope, milestones, dependencies, risks).
• Supporting the team lead in follow-up and coordination to bring stability to planning and execution.
• Contributing 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 the Data Platform team and substantive partners/stakeholders.
• Working in an environment with multiple priorities, where there is a need for structure in intake, follow-up, and communication.

Profile (must-haves):

• Master’s degree in IT
• Strong, hands-on experience as 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 at 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).
• Trilingualism (NL/FR/EN) is highly 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 major 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 methodical agreements.
• Better predictability and progress on important data projects and strategic initiatives.

Together with your CV, we ask you to submit the result of the exercise below. Failure to submit an answer or if the answers do not meet expectations will result in the candidate not being considered:

Explain how a random forest works and in which situations you would prefer XGBoost or AdaBoost compared to a Random Forest.