A major financial institution based in Brussels, Belgium, is expanding its Artificial Intelligence team with an Expert Fraud Data Scientist to improve the detection of financial crime in banking data. The role combines Python, SQL and fraud detection methods such as machine learning, graph analytics and behavioral analytics across large transactional and customer datasets.
The mission
The Artificial Intelligence team includes approximately 90 specialists across Data Science, Machine Learning Engineering, Business Analysis, Scrum Master, Product Ownership and management. It develops AI applications for customer assistance, employee productivity and internal automation, while fraud analytics turns collected, labelled, enriched and structured data into controls for banking operations. The technical landscape includes SQL, Python with Pandas, Spark/Hadoop and distributed data processing technologies for large-scale transactional and behavioral datasets.
As a Senior Data Scientist in Fraud Detection, you will work from data collection and feature engineering through model development, validation and continuous improvement. You will collaborate with Fraud Operations, Risk, Compliance and Product teams to translate emerging fraud patterns into scalable analytical solutions. Your work will support investigations into complex schemes and new attack vectors, and help the organisation reduce financial and reputational risk.
Your responsibilities
- Improve the quality of collected, labelled, structured and enriched data to increase the reliability of fraud models.
- Design, train, validate and refine fraud detection models using supervised and unsupervised machine learning, anomaly detection, graph analytics, network analysis and behavioral analytics.
- Transform large volumes of transactional and customer data into actionable fraud intelligence through feature engineering, data enrichment and pattern discovery.
- Investigate complex fraud schemes and emerging attack vectors, turning findings into practical detection strategies for Fraud Operations and Risk teams.
- Build data-centric AI solutions using robust software engineering practices and contribute to their deployment in production.
- Communicate analytical findings clearly to technical and business stakeholders, supporting decisions across Fraud, Compliance and Product.
Your profile
Essential skills
- At least 7 years of experience in data science, including substantial hands-on experience with fraud detection and AI applications.
- Ability to process large-scale datasets using SQL, Python, Pandas, Spark/Hadoop and distributed data processing technologies.
- Capability to apply supervised and unsupervised machine learning, anomaly detection, graph analytics, network analysis and behavioral analytics.
- Strong practice in feature engineering, data enrichment, data validation and pattern discovery across transactional and behavioral data.
- Experience developing data-centric AI solutions with software engineering practices suitable for production deployment.
- Ability to work autonomously, communicate analytical findings clearly and collaborate with Fraud Operations, Risk, Compliance and Product teams.
Preferred skills
- Experience in the financial sector, particularly in fraud analytics.
- Practical experience with generative AI.
- Java programming experience.
- PhD in statistics, computer science, engineering or another quantitative discipline.
Languages
- English, fluent, at CEFR C1 level or above.
Education
- Master’s degree in statistics, computer science, engineering or another quantitative field, or equivalent demonstrable analytical experience.