Business Data Analyst

Matrix is a global technology services and consulting company specializing in advanced technologies, software solutions, and digital products. The company is seeking an experienced Business Data Analyst to support sanctions, fraud, and AML model tuning, optimization, performance evaluation, and quantitative analysis. The role involves analyzing banking and transaction data, developing datasets and automation, and communicating findings to technical, business, regulatory, and senior-management stakeholders.

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