AML Business Data Analyst

Job Description: Data Analyst – Banking / Financial Crime Modeling

Position Overview

We are seeking an experienced Data Analyst with a strong banking background and expertise in financial crime analytics, including sanctions, fraud, or Anti-Money Laundering (AML) modeling. The ideal candidate will have strong quantitative and analytical capabilities, hands-on experience with model development and tuning, and advanced proficiency in Python and SQL.

This role will focus on analyzing complex banking data, developing and optimizing classification models, evaluating model performance, and supporting tuning initiatives to improve the effectiveness of financial crime detection systems.

Key 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 appropriate tuning methodologies and analytical tools to identify opportunities for model optimization.
• Develop and evaluate classification models, including logistic regression, multinomial logit, and tree-based machine learning algorithms such as XGBoost, LightGBM, and Random Forest.
• Use Python and SQL to perform data analysis, feature engineering, statistical modeling, and automation.
• Work with large and complex datasets to identify data quality issues, modeling gaps, and opportunities for improvement.
• Compare model performance using appropriate statistical measures and recommend optimization strategies.
• Document model tuning methodologies, results, performance metrics, and optimization activities.
• Prepare clear reports and present findings to senior management, regulatory stakeholders, and interdisciplinary teams.
• Collaborate with business, technology, compliance, and model governance teams to address analytical and modeling challenges.
• Maintain code quality and version control using tools such as GitHub.

Required Qualifications & Experience

• 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 and Multinomial Logit
• XGBoost
• LightGBM