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We are looking for an experienced Senior Data Scientist with strong expertise in Python, PySpark, and advanced analytics, along with a solid understanding of Financial Crime, Fraud Monitoring, and AML concepts. The ideal candidate will work on large-scale data to build, enhance, and optimize analytical and machine learning models used for fraud detection and financial crime prevention.
Job Responsibility:
Design, develop, and deploy data science and machine learning models for fraud detection, transaction monitoring, and financial crime use cases
Analyze large, complex datasets using Python and PySpark in distributed data environments
Build end-to-end analytics pipelines including data ingestion, feature engineering, model training, and validation
Apply statistical analysis, ML techniques, and pattern recognition to identify suspicious behaviors and emerging fraud typologies
Collaborate with business, compliance, and technology teams to translate financial crime requirements into analytical solutions
Monitor model performance, perform tuning, and ensure model stability and regulatory alignment
Document models, methodologies, and assumptions for internal governance and audit requirements
Stay updated on financial crime trends, fraud patterns, and regulatory expectations
Requirements:
5+ years of experience in Data Science, Analytics, or a related role
Strong proficiency in Python (NumPy, Pandas, Scikit-learn, etc.)
Hands-on experience with PySpark / Spark for large-scale data processing
Solid understanding of Financial Crime domains including: Fraud Monitoring, Transaction Monitoring, AML / CTF concepts, Customer risk and suspicious activity patterns
Experience building and validating machine learning models (supervised & unsupervised)
Strong knowledge of data preprocessing, feature engineering, and model evaluation
Ability to communicate complex analytical findings to non-technical stakeholders