Business Data Analyst

About the role

We are seeking an experienced Data Analyst with strong banking and financial-crime experience to support data analysis, model tuning, optimization, and performance evaluation initiatives across sanctions, fraud, and AML.

Responsibilities

• Analyze and support Sanctions, Fraud, and AML models, including model tuning, optimization, and performance monitoring.
• Apply appropriate model tuning methodologies and tools to improve model effectiveness and reduce false positives.
• Analyze banking products, processes, systems, and transaction data to identify trends, patterns, and potential data or modeling issues.
• Document model tuning methodologies, results, and optimization activities, and prepare reports and presentations for senior management and regulatory stakeholders.
• Perform quantitative analysis to evaluate model performance and identify opportunities for improvement.
• Develop and analyze features and datasets to support statistical modeling and machine learning initiatives.
• Collaborate with data scientists, model risk teams, technology teams, compliance professionals, and business stakeholders.
• Work with large datasets and develop automation to improve data analysis and model-tuning processes.
• Clearly document and communicate analytical findings and recommendations to interdisciplinary teams.
• Manage multiple priorities effectively in a fast-paced environment with changing business requirements.

Requirements

• Experience in Sanctions, Fraud, AML, or financial-crime modeling.
• Strong understanding of model tuning methodologies, optimization techniques, and related tools.
• Strong knowledge of banking products, processes, systems, and financial transactions.
• Advanced proficiency in Python and SQL, including experience working with large datasets and developing automated analytical solutions.
• Strong Python programming skills for data analysis, feature engineering, statistical analysis, and modeling.
• Experience developing and evaluating classification models, including: Logistic Regression, Multinomial Logistic Regression, XGBoost, LightGBM, Random Forest.
• Ability to compare model performance using appropriate statistical and model-evaluation techniques.
• Strong understanding of statistics, data science, and quantitative analysis.
• Ability to identify, troubleshoot, and resolve data-quality and modeling issues.
• Experience with version control systems such as Git/GitHub.
• Master's or Ph.D. in Statistics, Economics, Finance, Mathematics, Data Science, or a related quantitative discipline.
• Excellent written and verbal communication skills, with the ability to explain technical findings to both technical and non-technical stakeholders.
• Strong organizational skills and the ability to manage multiple projects and shifting priorities effectively.

Benefits

• Comprehensive benefits package, including medical, dental, 401K, STD, HSA, PTO, and more.

Pay

Competitive base salary.