Introductie
The Data Mining Team is looking for a senior profile who will fulfill a dual mission:
• Accelerate strategic data initiatives through subject matter expertise, technical
leadership and coaching.
• Manage the continuous flow of ad-hoc data requests through prioritization,
structuring, consolidation and translation into reusable solutions and data products.
The role brings seniority, structure and technical depth to the Data Mining Team, while also
supporting operations and follow-up together with the team lead and other stakeholders.
Organisatie
• Work closely with the Data Mining Team (data scientists, analysts and, where
relevant, data engineers and platform stakeholders).
• Collaborate with the Data Platform Team and business stakeholders.
• Operate in an environment with multiple priorities where structure in intake, follow-
up and communication is essential.
Functie
1. Strategic Projects & Technical Leadership
• Take the technical lead in complex data initiatives (e.g. advanced analytics,
graph/network analytics, integrations, architectural decisions).
• Help shape the approach, solutioning and priorities of larger initiatives, with
attention to feasibility, impact and scalability.
• Safeguard and promote quality standards, including reproducibility, documentation,
methodology and, where relevant, engineering quality.
2. Team Uplift & Co-Creation (within the Data Mining Team)
• Coach and support data scientists and analysts through co-creation, technical reviews
and sharing best practices.
• Contribute structurally to increasing team competencies (methodology, ways of
working, quality and communication).
• Take an active role in defining team agreements, such as Definition of Done, working
methods and knowledge sharing.
3. Structuring & Productizing Ad-Hoc Requests
• Create visibility and structure around incoming requests through intake,
prioritization, status tracking and communication.
• Cluster ad-hoc work and transform it, where possible, into reusable and scalable
solutions (datasets, analytical methods, templates and data products).
• Apply FAIR principles from a data product perspective with a focus on quality and
reusability.
4. Project Management & Follow-Up (Stretch)
• Take ownership of basic project and delivery follow-up activities (scope, milestones,
dependencies and risks).
• Support the team lead in coordination and follow-up activities to bring stability to
planning and execution.
• Contribute to stakeholder alignment, expectation management, decision-making and
escalations where needed.
Functie-eisen
Must-Haves
• Master’s degree in IT.
• Strong hands-on experience as a Data Scientist and/or ML Engineer with a focus on
Python.
• Experience with data analysis and modelling (Pandas, Scikit-learn) and
building/improving machine learning models in production environments.
• Strong software engineering foundation: Git, code reviews, CI/CD pipelines and
Docker.
• Experience building APIs and reusable components (e.g. FastAPI).
• Knowledge of SQL.
• Experience with Infrastructure-as-Code and/or cloud technologies is a plus
(Terraform, AWS, GCP).
• Strong ability to structure ambiguous requests and translate them into concrete
approaches and deliverables.
• Experience coaching and mentoring colleagues and working in co-creation
environments (e.g. technical coaching, reviews, Scrum/Scrum Master activities).
• Strong communication skills, stakeholder management and expectation
management.
• Fluency in Dutch, French and English is highly preferred.
Nice-to-Have
• Experience with data product thinking, governance and quality principles (FAIR,
documentation, definitions and reusability).
• Experience with graph analytics, network analytics or other advanced analytics
domains.
• Experience with Databricks.
• Previous experience within a public social security or government-related
environment is considered a strong asset.
• Experience with secondary data use and fraud detection.
Expected Impact (3–6 Months)
• A clearer intake and prioritization process for ad-hoc requests directed to the Data
Mining Team.
• More reusable and scalable outputs instead of one-off solutions.
• Measurable improvement in team quality through coaching, reviews, and
methodological standards.
• Better predictability and progress on key data initiatives and strategic projects.
Together with your CV, we ask you to submit the result of the exercise below. Failure to
submit an answer, or answers that do not meet expectations, will result in the candidate not
being considered:
Please explain how a Random Forest works and in which situations you would prefer XGBoost or AdaBoost
compared to a Random Forest.
Inlichtingen
Ginny-Rose Lie-A-Jen +32 3 202 05 00