AML Business Data Analyst

Matrix Global

. Perform quantitative analysis and model tuning across sanctions, fraud, and AML use cases .

Posted 11/3/2026full-timeRemote • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in developing, validating, and optimizing statistical and machine learning models for fraud, sanctions, and AML use cases, with advanced proficiency in Python and SQL for data analysis and feature engineering. Strong analytical and problem-solving skills, coupled with effective communication and collaboration abilities across interdisciplinary teams.

Highest-signal resume keywords

Statistical ModelingMachine Learning Model DevelopmentPython ProgrammingSQL ProficiencyModel Tuning Methodologies

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Statistical ModelingMachine LearningLogistic RegressionMultinomial LogitXGBoostLightGBMRandom ForestData AnalysisFeature EngineeringModel Performance Evaluation

Soft Skills

Analytical SkillsProblem-Solving SkillsCommunication SkillsCollaboration SkillsAdaptability

Tools & Technologies

GitGitHubStatistical ToolsAnalytical ToolsAutomation Tools

Industry Keywords

BankingFinancial ServicesFraud ModelingAML ModelingSanctions

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

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 model optimization opportunities
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 improvement opportunities
Compare model performance using 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
Maintain code quality and version control using GitHub

Requirements

What you’ll need

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, Multinomial Logit, XGBoost, LightGBM, and Random Forest
Advanced proficiency in Python and SQL, including 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 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

Benefits

Comp & perks

Competitive compensation and benefits
Meaningful career development opportunities
Challenging and engaging work environment
Diverse, inclusive culture
Collaboration, continuous learning, and growth