AML Business Data Analyst
New
J
JobgetherFinancial services
Based in the United StatesFull-Time
Salary not disclosed
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Job Details
- Required Skills
- PythonSQLGit
Requirements
- Master’s degree or Ph.D. in Statistics, Economics, Finance, Mathematics, Data Science, or another relevant quantitative discipline.
- Professional experience in banking or financial services, preferably involving AML, sanctions, or fraud modeling.
- Strong understanding of financial crime risks, banking products, processes, and systems.
- Hands-on experience developing, validating, and evaluating statistical and machine learning classification models.
- Practical knowledge of logistic regression, multinomial logit, XGBoost, LightGBM, and Random Forest.
- Advanced proficiency in Python and SQL, including writing production-quality code for data analysis, feature engineering, and statistical modeling.
- Strong knowledge of model tuning methodologies, optimization techniques, statistical concepts, and model performance evaluation.
- Experience processing and analyzing large datasets and implementing automation solutions.
- Familiarity with version control tools such as Git and GitHub.
- Ability to investigate and resolve data quality issues and modeling challenges.
- Technical documentation and reporting skills, including explaining model tuning outcomes to senior management and regulators.
- Ability to present complex analytical findings clearly and collaborate with interdisciplinary teams.
Responsibilities
- Conduct quantitative analysis and tune models for AML, sanctions, and fraud detection.
- Develop, validate, and optimize statistical and machine learning models.
- Build and evaluate classification models using logistic regression, multinomial logit, XGBoost, LightGBM, and Random Forest.
- Use Python and SQL for data analysis, feature engineering, statistical modeling, and process automation.
- Analyze large datasets to identify data quality issues, modeling gaps, trends, and improvement opportunities.
- Compare model performance using relevant statistical measures and recommend improvements.
- Document model development and tuning methods, analytical results, performance metrics, and optimization activities.
- Prepare reports and present findings to senior management, regulatory stakeholders, and cross-functional teams.
- Collaborate with business, technology, compliance, and model governance teams to address analytical and modeling challenges.
- Maintain code quality, documentation, and version control using Git and GitHub.
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