AML Modeling Specialist

Banco Santander•Hartford, CT

25d

About The Position

As an AML Modeling Specialist, you play a key role in safeguarding the bank’s integrity and ensuring compliance with regulatory expectations. You design, enhance, and maintain analytical models that identify suspicious activity, strengthen the bank’s financial crime detection framework, and drive continuous improvement in AML monitoring capabilities. Your insights directly support the bank’s mission to protect customers and uphold trust across the global financial system.

Requirements

Bachelor's Degree or equivalent work experience: Statistics, Mathematics, Economics or equivalent quantitative discipline. Required
Master's Degree Statistics, Mathematics, Economics or equivalent quantitative discipline. Preferred
3+ Years Risk Management, Risk Modeling, AML Req
Deep understanding of AML processes, transaction monitoring systems, and financial crime risk.
Proficiency in data analysis, model development, and statistical tools (e.g., Python, SAS, SQL, or R).
Collaborative approach and ability to communicate technical findings to non-technical stakeholders.
Commitment to operational excellence, model integrity, and regulatory compliance.
Strong analytical mindset with the ability to translate complex data into actionable insights.

Nice To Haves

Established work history or equivalent demonstrated through a combination of work experience, training, military service, or education.
Experience in Microsoft Office products.

Responsibilities

Develop and implement AML detection and segmentation models using statistical and machine learning techniques.
Partner with compliance, risk, and data science teams to ensure model outputs align with business objectives and regulatory standards.
Analyze transaction data to identify emerging money laundering patterns and typologies.
Perform model validation, back-testing, and performance monitoring to ensure accuracy and effectiveness.
Prepare model documentation, regulatory submissions, and audit materials.
Support ongoing model governance, tuning, and optimization efforts to reduce false positives and improve efficiency.