Senior Data Scientist Lead – AML & Financial Crime Analytics

Matrix Global•USA,

About The Position

Matrix IFS is seeking a Senior Data Scientist Lead to drive the design and development of advanced analytics and machine learning solutions supporting Anti-Money Laundering (AML), Financial Crime, Fraud Detection, and Risk Management initiatives. This role combines deep expertise in data science, statistical modeling, and financial crime compliance to develop scalable analytical solutions that enhance risk detection, regulatory compliance, and operational efficiency.

Requirements

Master's degree (or higher) in Data Science, Statistics, Mathematics, Computer Science, Economics, Engineering, or another quantitative discipline.
Proven experience developing machine learning and statistical models within AML, Financial Crime, Fraud Analytics, Risk Analytics, or Regulatory Compliance environments.
Hands-on experience supporting one or more of the following: Anti-Money Laundering (AML), Know Your Customer (KYC) / Customer Due Diligence (CDD), Sanctions Screening, Transaction Monitoring, Fraud Detection and Prevention, Financial Crime Compliance.
Strong expertise in machine learning, predictive modeling, statistical analysis, and quantitative research.
Experience working with large-scale structured and unstructured datasets.
Excellent communication skills with the ability to explain complex analytical concepts to technical and non-technical stakeholders.

Nice To Haves

Experience with model governance, model validation, and regulatory frameworks applicable to financial institutions.
Knowledge of financial services, banking operations, and emerging financial crime typologies.
Experience mentoring technical teams and leading complex analytics initiatives.

Responsibilities

Design, develop, deploy, and validate machine learning models for AML transaction monitoring, fraud detection, customer risk scoring, sanctions screening, customer segmentation, and anomaly detection.
Build scalable end-to-end model development pipelines, including data preparation, feature engineering, model training, validation, deployment, and ongoing performance monitoring.
Optimize model performance through cross-validation, hyperparameter tuning, benchmarking, and continuous model refinement.
Apply advanced statistical methods, predictive analytics, Bayesian modeling, and quantitative techniques to identify financial crime risks and emerging behavioral patterns.
Develop simulation models, risk assessment frameworks, and analytical methodologies to support regulatory compliance and business decision-making.
Analyze large, complex datasets to uncover actionable insights and improve detection effectiveness.
Develop analytical solutions supporting AML, KYC/CDD, sanctions screening, transaction monitoring, fraud prevention, and broader financial crime compliance programs.
Evaluate model effectiveness using appropriate performance metrics and ensure alignment with regulatory expectations and model governance standards.
Support model documentation, validation, audit reviews, and regulatory examinations.
Partner with Compliance, AML Operations, Fraud, Risk Management, Data Engineering, and Technology teams to translate business requirements into data-driven solutions.
Present analytical findings, model performance, and strategic recommendations to senior leadership through reports, dashboards, and executive presentations.
Mentor junior data scientists and contribute to best practices in model development, governance, and analytics.