AML Business Data Analyst

📋 Description

• Conduct quantitative analysis and model tuning across AML, sanctions, and fraud detection use cases.
• Develop, validate, and optimize statistical and machine learning models to improve detection
• Build and evaluate classification models using logistic regression, multinomial logit, XGBoost
• Apply appropriate model tuning methodologies, optimization techniques, and analytical tools to
• Use Python and SQL for data analysis, feature engineering, statistical modeling, and process
• Analyze large and complex datasets to identify data quality issues, modeling gaps, trends, and

🎯 Requirements

• Master’s degree or Ph.D. in Statistics, Economics, Finance, Mathematics, Data Science, or another
• Professional experience in banking or financial services, preferably involving AML, sanctions, or
• Strong understanding of financial crime risks, banking products, processes, and systems.
• Hands-on experience developing, validating, and evaluating statistical and machine learning
• Practical knowledge of logistic regression, multinomial logit, XGBoost, LightGBM, and Random Forest.
• Advanced proficiency in Python and SQL, including the ability to write production-quality code for

🎁 Benefits

• Competitive compensation; specific salary range not disclosed.
• Opportunity to work on complex financial crime analytics and modeling initiatives.
• Exposure to banking, sanctions, fraud prevention, and AML technology projects.
• Collaborative, international, and fast-growing professional environment.
• Opportunities for career development and continuous learning.
• A culture that values innovation, teamwork, and professional growth.