Data Scientist

Contract: 6 to 12 months (possibility of extension)

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.

• 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

• 5+ years of experience in Data Science, Analytics, or a related role
• Strong proficiency in Python (NumPy, Pandas, Scikit-learn, xcfaprz etc.)
• Hands-on experience with PySpark / Spark for large-scale data processing
• Solid understanding of Financial Crime domains including:
  • Customer risk and suspicious activity patterns
  • Experience building and validating machine learning models (supervised & unsupervised)
• Solid knowledge of data preprocessing, feature engineering, and model evaluation
• Ability to communicate complex analytical findings to non-technical stakeholders