AML Business Data Analyst

Matrix USA is seeking a senior data analyst for banking and financial crime modeling. The role analyzes banking data, develops and tunes classification models, evaluates performance, and supports improvements to sanctions, fraud, and AML detection systems.

Responsibilities:

• Perform quantitative analysis and model tuning across sanctions, fraud, and AML use cases
• Develop, validate, and optimize statistical and machine learning models to improve detection accuracy and performance
• Apply appropriate tuning methodologies and analytical tools to identify opportunities for model optimization
• Develop and evaluate classification models, including logistic regression, multinomial logit, and tree-based machine learning algorithms such as XGBoost, LightGBM, and Random Forest
• Use Python and SQL to perform data analysis, feature engineering, statistical modeling, and automation
• Work with large and complex datasets to identify data quality issues, modeling gaps, and opportunities for improvement
• Compare model performance using appropriate statistical measures and recommend optimization strategies
• Document model tuning methodologies, results, performance metrics, and optimization activities
• Prepare clear reports and present findings to senior management, regulatory stakeholders, and interdisciplinary teams
• Collaborate with business, technology, compliance, and model governance teams to address analytical and modeling challenges
• Maintain code quality and version control using tools such as GitHub

Requirements:

• an experienced Data Analyst with a strong banking background and expertise in financial crime analytics, including sanctions, fraud, or Anti-Money Laundering (AML) modeling
• strong quantitative and analytical capabilities
• hands-on experience with model development and tuning
• advanced proficiency in Python and SQL
• Master's or Ph.D. in Statistics, Economics, Finance, Mathematics, Data Science, or a related quantitative discipline
• Experience in the banking or financial services industry, preferably in sanctions, fraud, or AML modeling
• Strong understanding of model tuning methodologies, optimization techniques, and performance evaluation
• Good knowledge of banking products, processes, systems, and financial crime risk
• Hands-on experience developing and evaluating classification and machine learning models, including:
• Logistic Regression and Multinomial Logit
• XGBoost
• LightGBM
• Random Forest
• Advanced proficiency in Python and SQL, including the ability to write production-quality Python code for data analysis, feature engineering, and statistical modeling
• Experience working with large datasets, automation, and data processing
• Strong understanding of statistical concepts, data science methodologies, and model performance evaluation
• Familiarity with version control tools such as Git and GitHub
• Strong quantitative, analytical, and problem-solving skills
• Ability to identify, investigate, and resolve data quality and modeling issues
• Excellent documentation and reporting capabilities, including the ability to communicate model tuning outcomes to senior management and regulators
• Strong verbal and written communication skills
• Ability to collaborate effectively with cross-functional and interdisciplinary teams
• Adaptability and the ability to manage multiple priorities in a changing environment