Business Data Analyst

Matrix Global

. Analyze and support Sanctions, Fraud, and AML models, including model tuning, optimization, and performance monitoring .

Posted 9/22/2026full-timeRemote • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Sanctions, Fraud, and AML modeling, with a strong focus on model tuning methodologies, statistical analysis, and automation of analytical solutions. Proficient in Python and SQL for data analysis and feature engineering, with the ability to communicate complex findings to diverse stakeholders.

Highest-signal resume keywords

Sanctions, Fraud, AML ModelingModel Tuning MethodologiesPython ProgrammingStatistical AnalysisClassification Models

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Model TuningOptimization TechniquesStatistical ModelingFeature EngineeringClassification ModelsLogistic RegressionXGBoostLightGBMRandom ForestQuantitative Analysis

Soft Skills

Excellent Communication SkillsOrganizational SkillsAbility to Manage Multiple Projects

Tools & Technologies

PythonSQLGitGitHub

Certifications & Qualifications

Master's or Ph.D. in StatisticsEconomicsFinanceMathematicsData Science

Industry Keywords

Banking ProductsFinancial TransactionsData QualityModel EvaluationAutomation

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

Analyze and support Sanctions, Fraud, and AML models, including model tuning, optimization, and performance monitoring
Apply 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
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
Document and communicate analytical findings and recommendations to interdisciplinary teams
Manage multiple priorities in a fast-paced environment with changing business requirements

Requirements

What you’ll need

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, and 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

Comp & perks

Competitive base salary
Comprehensive benefits package, including medical, dental, 401K, STD, HSA, PTO, and more
Meaningful career development opportunities
Challenging and engaging work environment
Diverse, inclusive culture that encourages collaboration, continuous learning, and growth