AML Business Data Analyst

This is a fully remote position, open to applicants in United States.

📋 Description

• Conduct quantitative analysis and model refinement across use cases related to sanctions, fraud, and AML.

• Create, validate, and enhance statistical and machine learning models to boost detection accuracy and overall performance.

• Utilize tuning methodologies and analytical tools to pinpoint opportunities for model optimization.

• Develop and assess classification models, including logistic regression, multinomial logit, XGBoost, LightGBM, and Random Forest.

• Employ Python and SQL for data analysis, feature engineering, statistical modeling, and automation tasks.

• Examine extensive and intricate datasets to detect data quality issues, modeling deficiencies, and areas for improvement.

• Evaluate model performance using statistical metrics and propose optimization strategies.

• Document model tuning methodologies, outcomes, performance metrics, and optimization efforts.

• Generate reports and present insights to senior management, regulatory bodies, and interdisciplinary teams.

• Work collaboratively with business, technology, compliance, and model governance teams.

• Ensure code quality and version control by utilizing GitHub.

⛳️ Requirements

• Master’s degree or Ph.D. in Statistics, Economics, Finance, Mathematics, Data Science, or a related quantitative field.

• Experience in the banking or financial services sector, ideally in sanctions, fraud, or AML modeling.

• Profound understanding of model tuning methodologies, optimization techniques, and performance evaluation criteria.

• Solid knowledge of banking products, processes, systems, and financial crime risk management.

• Practical experience in developing and assessing classification and machine learning models, including Logistic Regression, Multinomial Logit, XGBoost, LightGBM, and Random Forest.

• Advanced skills in Python and SQL, including the capability to produce high-quality Python code for data analysis, feature engineering, and statistical modeling.

• Experience with large datasets, automation processes, and data handling.

• Strong grasp of statistical concepts, data science methodologies, and model performance assessment.

• Familiarity with version control tools such as Git and GitHub.

• Exceptional quantitative, analytical, and problem-solving abilities.

• Capability to identify, investigate, and rectify data quality and modeling concerns.

• Excellent documentation and reporting skills, including the ability to communicate model tuning results to senior management and regulators.

• Strong verbal and written communication skills.

• Proven ability to collaborate effectively with cross-functional and interdisciplinary teams.

• Flexibility and capacity to manage multiple priorities in a dynamic environment.

🏝️ Benefits

• Competitive salary and benefits package.

• Meaningful opportunities for career development.

• A challenging and stimulating work environment.

• A culture that values diversity and inclusivity.

• Opportunities for collaboration, continuous learning, and professional growth.