AML Business Data Analyst

Jobgether; United States; Remote; USD 110,000 - 170,000; Full time

Benefits offered by this job

Competitive compensation

Career development

International team

Continuous learning

Job summary

Jobgether in the United States seeks an AML Business Data Analyst to strengthen financial crime detection through advanced analytics. You will develop, validate, and optimize models for AML, sanctions, and fraud, using Python and SQL to handle large banking datasets.

Collaboration across technology, compliance, and governance teams is essential. You will communicate model performance to senior management and regulators, document methodology, and contribute to automated data processes and

Qualifications

• Master's or Ph.D. in Statistics, Economics, Finance, Mathematics, Data Science, or similar.
• Professional experience in banking or financial services with AML, sanctions, or fraud modeling.
• Strong understanding of financial crime risks and banking processes.
• Hands-on experience developing, validating, and evaluating ML classification models.
• Proficiency in Python and SQL for production-quality data analysis and modeling.
• Knowledge of model tuning, optimization, and evaluation methodologies.
• Experience processing large datasets and implementing automation.
• Familiarity with Git and GitHub version control.
• Strong communication and documentation skills.

Responsibilities

• Conduct quantitative analysis and model tuning across AML, sanctions, and fraud detection.
• Develop, validate, and optimize statistical and ML models to improve detection.
• Build and evaluate classifiers using logistic regression, XGBoost, LightGBM, RF.
• Apply tuning methodologies and optimization techniques to improve performance.
• Use Python and SQL for data analysis, feature engineering, and automation.
• Analyze large datasets to identify data quality issues and modeling gaps.
• Document methodologies, results, and metrics.
• Prepare reports and present findings to senior management and regulators.
• Collaborate with cross-functional teams to resolve modeling challenges.
• Maintain code quality and version control with Git/GitHub.

Skills

Python

SQL

ML modeling

Statistical analysis

XGBoost

LightGBM

Random Forest

Logistic Regression

Feature Engineering

Model Evaluation

Data Analysis

Git & GitHub

Communication

Education

Master's or Ph.D. in Statistics/Data Science/Quantitative field

Tools

Git

GitHub

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an AML Business Data Analyst based in the United States.

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.

Accountabilities:

• 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:

• 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.

Benefits:

• Competitive compensation; specific salary range not disclosed.
• Opportunity to work on complex financial crime analytics and modeling initiatives.
• Exposure to banking, sanctions, fraud prevention, and AML technology projects.
• Collaborative, international, and fast-growing professional environment.
• Opportunities for career development and continuous learning.
• A culture that values innovation, teamwork, and professional growth.
• Specific healthcare, retirement, paid leave, and other benefits are not detailed in the posting.