AML Business Data Analyst

Matrix Global • USA,

2d

About The Position

Matrix is seeking an experienced Data Analyst with a strong banking background and expertise in financial crime analytics, including sanctions, fraud, or Anti-Money Laundering (AML) modeling. The role focuses on analyzing complex banking data, developing and optimizing classification models, evaluating model performance, and supporting tuning initiatives to improve financial crime detection systems.

The analyst will work with business, technology, compliance, and model governance teams, and will document and communicate modeling methods, results, and optimization recommendations to senior management, regulatory stakeholders, and interdisciplinary teams.

Requirements

• Master's or Ph.D. in Statistics, Economics, Finance, Mathematics, Data Science, or a related quantitative discipline.
• Experience in banking or financial services, 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, multinomial logit, XGBoost, LightGBM, and Random Forest.
• Advanced proficiency in Python and SQL, including production-quality Python coding 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 communicating 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 ability to manage multiple priorities in a changing environment.

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 tuning methodologies and analytical tools to identify opportunities for model optimization.
• Develop and evaluate classification models, including logistic regression, multinomial logit, XGBoost, LightGBM, and Random Forest.
• Use Python and SQL for data analysis, feature engineering, statistical modeling, and automation.
• Analyze 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 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.

Benefits

• Competitive compensation and benefits
• Meaningful career development opportunities
• Challenging and engaging work environment
• Diverse and inclusive culture that encourages collaboration, continuous learning, and growth

What This Job Offers

• Job Type: Full-time
• Career Level: Senior