DescriptionData Analyst - Banking / Financial Crime
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.
Key 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.
Required Qualifications
- 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:- Competitive base salary.
- Comprehensive benefits package, including medical, dental, 401K, STD, HSA, PTO, and more.
Join Us:Come and join a winning team! You'll be challenged, have fun, and be part of a highly respected organization.
