AML Business Data Analyst
Core
Develop, validate, and optimize statistical and machine learning models for Anti-Money Laundering (AML), sanctions screening, and fraud prevention in banking and financial services.
Role type
Senior IC machine-learning engineer (financial crime)
Builds
Production ML models for risk detection
Domain
Banking / Financial Services / Financial Crime
Deliverable
production ML models
Required skills
Python, SQL, Logistic Regression, Multinomial Logit, XGBoost, LightGBM, Random Forest, Statistical Modeling, Feature Engineering, Model Tuning, Data Quality Analysis
Preferred skills
Git, GitHub, Version Control
Responsibilities
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 accuracy; Build and evaluate classification models using logistic regression, multinomial logit, XGBoost, LightGBM, and Random Forest algorithms; Use Python and SQL for data analysis, feature engineering, statistical modeling, and process automation; Analyze large and complex datasets to identify data quality issues, modeling gaps, trends, and opportunities for enhancement; Prepare clear reports and present findings to senior management, regulatory stakeholders, and cross-functional teams.
