AML Business Data Analyst
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Job Description
AML data analyst developing and tuning banking financial crime models for Matrix, a global technology services company. Applying Python, SQL, and machine learning to improve 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 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:
- 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:
- Competitive compensation and benefits
- Meaningful career development opportunities
- Challenging and engaging work environment
- Diverse, inclusive culture
- Collaboration, continuous learning, and growth
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