Remote Fraud Data Analyst: AI & Payments Risk

EBA CLEARING; Brussel Hoofdstad; Sur place; EUR 60 000 - 90 000; Plein temps

Avantages offerts par ce poste

Competitive salary package

Sports & cultural activities budget

Meal tickets

Professional development budgets

Internal mobility opportunities

Flexible work from home

Well-being initiatives

Résumé du poste

EBA CLEARING is seeking a skilled data science professional to join the FPAD Competence Centre in Brussels or Frankfurt. You will develop and maintain fraud detection models, analyze data, and support users in understanding FPAD results and cases.

Responsibilities include monitoring model quality, exploring fraud patterns, building detection features, and collaborating with European financial institutions. Fluency in English is required.

Qualifications

• University degree in computer science, mathematics or related field.
• At least three years of experience in a quantitative role in a fraud or risk environment at a bank or PSP.
• Strong knowledge of data science and data engineering, preferably with Python.
• Experience to optimise models on response time.

Responsabilités

• Monitor model and data quality in production.
• Find new emerging fraud characteristics by exploring transaction data.
• Build new features to detect fraudulent transactions.
• Develop state-of-the-art AI models against billions of historical transactions.
• Create documentation and charts to explain work to users.
• Share insights with experts from financial institutions across Europe.
• Stay up to date with fraud detection trends and drive solution development.

Connaissances

Intercultural communication

Customer-first attitude

Analytical thinking

Anticipate problems

Attention to detail

Autonomous work

Formation

University degree in computer science or related field

Outils

Python

SQL

Description du poste

EBA CLEARING is seeking a skilled data science professional to join the FPAD Competence Centre in Brussels or Frankfurt. You will develop and maintain fraud detection models, analyze data, and support users in understanding FPAD results and cases.

Responsibilities include monitoring model quality, exploring fraud patterns, building detection features, and collaborating with European financial institutions. Fluency in English is required.