Senior Data Scientist

Senior Data Scientist / Technical Lead Role Overview We are seeking a Senior Data Scientist to provide technical leadership, coaching, and strategic direction within a Data Mining team. The role combines hands-on advanced analytics and machine learning expertise with stakeholder management, team mentoring, and the transformation of ad-hoc analytical requests into scalable data products and reusable solutions.

Key Responsibilities
Technical Leadership & Strategic Initiatives Lead complex data science and machine learning projects from design to production. Drive architectural and technical decisions for advanced analytics initiatives. Define and promote best practices for reproducibility, documentation, code quality, and data science methodologies. Support the development of scalable and maintainable data products. Team Coaching & Knowledge Sharing Mentor data scientists and analysts through code reviews, technical coaching, and collaborative problem-solving. Improve team capabilities in analytics, machine learning, software engineering, and delivery practices. Contribute to team processes, standards, and knowledge-sharing initiatives. Data Products & Analytics Delivery Structure, prioritize, and manage incoming analytical requests. Transform one-off analyses into reusable datasets, models, templates, and data products. Apply data governance and FAIR principles to improve data quality, accessibility, and reusability. Develop and maintain machine learning solutions in production environments. Stakeholder & Project Coordination Collaborate with business stakeholders, data engineers, platform teams, and management. Support planning, risk management, prioritization, and delivery tracking. Communicate technical concepts clearly to both technical and non-technical audiences. Required Skills & Experience Master's degree in Computer Science, Data Science, Engineering, or a related field. Strong hands-on experience as a Data Scientist or ML Engineer. Advanced Python expertise, including Pandas, Scikit-learn, and machine learning frameworks. Experience developing, deploying, and maintaining production-grade ML models. Strong software engineering background with Git, CI/CD, Docker, APIs, and reusable service architectures (e.g., FastAPI). Solid SQL skills and experience with data modeling and data analysis. Experience with Agile/Scrum methodologies and mentoring technical teams. Excellent analytical, communication, and stakeholder management skills. Nice to Have Experience with Databricks. Knowledge of graph analytics, network analytics, or advanced analytics techniques. Experience with AWS, GCP, Terraform, and cloud-native architectures. Knowledge of data product management, governance, and FAIR principles. Experience in fraud detection, secondary data use, or highly regulated environments. Languages Dutch, French, and English (professional working proficiency preferred). Work Environment Hybrid working model.

Location:
Brussels, Belgium. Collaboration with multidisciplinary data, analytics, and platform teams. Candidate Assessment Candidates may be required to complete a technical exercise demonstrating their understanding of machine learning concepts and model selection approaches, including ensemble learning techniques such as Random Forest, XGBoost, and AdaBoost.