AML Business Data Analyst

United StatesRemoteFull-timemid

Data AnalystData

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Requirements Summary

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,

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Data AnalystData

The AML Business Data Analyst will apply advanced quantitative analysis and machine learning techniques to strengthen financial crime detection across banking and financial services. This role focuses on developing, validating, and optimizing analytical models for Anti-Money Laundering (AML), sanctions screening, and fraud prevention. You will work with large and complex banking datasets, using Python and SQL to identify patterns, improve data quality, and enhance model performance. The position combines statistical expertise, hands-on model development, and business understanding to support more accurate and effective risk detection. Working closely with technology, compliance, and model governance teams, you will translate analytical findings into practical recommendations. You will also communicate model performance and optimization outcomes to senior management and regulatory stakeholders.

Conduct quantitative analysis and model tuning across AML, sanctions, and fraud detection use cases.
Develop, validate, and optimize statistical and machine learning models to improve detection accuracy and overall performance.
Build and evaluate classification models using logistic regression, multinomial logit, XGBoost, LightGBM, and Random Forest algorithms.
Apply appropriate model tuning methodologies, optimization techniques, and analytical tools to identify opportunities for improvement.
Use Python and SQL for data analysis, feature engineering, statistical modeling, and process automation.
Analyze large and complex datasets to identify data quality issues, modeling gaps, trends, and opportunities for enhancement.
Compare model performance using relevant statistical measures and recommend strategies to improve effectiveness.
Document model development and tuning methodologies, analytical results, performance metrics, and optimization activities.
Prepare clear reports and present findings to senior management, regulatory stakeholders, and cross-functional teams.
Collaborate with business, technology, compliance, and model governance teams to resolve analytical and modeling challenges.
Maintain high standards of code quality, documentation, and version control using tools such as Git and GitHub.

Requirements

~1 min read

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 the ability to write 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.
Strong quantitative reasoning, analytical thinking, and problem-solving capabilities.
Ability to investigate and resolve data quality issues and modeling challenges.
Excellent technical documentation and reporting skills, including the ability to explain model tuning outcomes to senior management and regulators.
Strong written and verbal communication skills, with the ability to present complex analytical findings clearly.
Ability to collaborate effectively with interdisciplinary teams and manage multiple priorities in a changing environment.