AML Compliance Model Developer – Associate

Banco Santander•Boston, MA

9h•Onsite

About The Position

Santander is a global leader and innovator in the financial services industry and is evolving from a high-impact brand into a technology-driven organization. Our people are at the heart of this journey and together, we are driving a customer-centric transformation that values bold thinking, innovation, and the courage to challenge what’s possible. This is more than a strategic shift. It’s a chance for driven professionals to grow, learn, and make a real difference. If you are interested in exploring the possibilities We Want to Talk to You!

Requirements

Bachelor’s degree in computer science, Information Systems, Data Analytics, Finance, Mathematics, or related field.
3–5 years of experience in AML Compliance Technology, AML model development, or a related field within banking or financial services.
Strong programming and analytical experience using SAS for data analysis, reporting, model support, and testing.
Good working knowledge of Python for data manipulation, scripting, automation, and analytical processing.
Strong SQL skills with experience developing complex queries, joins, procedures, and handling large-scale datasets.
Experience working with Jira for Agile project tracking, sprint management, and defect lifecycle management.
Working knowledge of testing methodologies including functional testing, regression testing, SIT, and UAT.
Understanding of AML regulations and compliance frameworks including BSA/AML, OFAC, KYC, and transaction monitoring concepts.
Strong analytical, quantitative, and problem-solving skills.
Ability to multitask, manage priorities, and work effectively in fast-paced environments.
Strong communication, documentation, and stakeholder management skills.

Nice To Haves

Experience working with AML transaction monitoring systems, alert analysis, and compliance technology platforms.
Experience in developing AI Agents.
Good understanding of RAG and MCP’s.
Experience with Oracle, relational databases, or ETL processes.
Exposure to EDD Verifier, ECS, and Temenos (T24) platforms.
Exposure to model validation, model governance, or threshold optimization initiatives.
Experience working with large-scale financial or transactional datasets.
Knowledge of version control tools such as Git.
Experience in cloud or distributed data environments is a plus.
Advanced Microsoft Office skills including Excel and PowerPoint.

Responsibilities

Develop, enhance, and maintain AML compliance models and transaction monitoring scenarios across AML compliance systems including advanced machine learning and AI models.
Apply data science, statistical modeling, natural language processing, and machine learning techniques to large structured and unstructured financial datasets to support AML model development, feature engineering, model validation, performance monitoring, and explainability.
Support AML compliance initiatives through model development, tuning, threshold optimization, and alert reduction strategies.
Perform advanced data analysis using SAS, SQL, and Python to identify trends, anomalies, suspicious patterns, and data quality issues across large financial datasets.
Design and execute complex SQL queries, stored procedures, data extraction, and reconciliation activities to support AML investigations and model testing.
Develop Python-based automation scripts and analytical solutions to improve operational efficiency and testing effectiveness.
Execute model testing activities including SIT, UAT, regression testing, defect validation, and production verification.
Collaborate with Compliance, Technology, Model Risk, and Business teams to gather requirements and implement AML monitoring solutions.
Analyze model assumptions, data integrity, and transaction monitoring effectiveness to ensure compliance with regulatory expectations.
Create and maintain technical documentation, testing evidence, Jira stories, defect logs, and implementation artifacts.
Work within Agile delivery methodologies and actively track development/testing activities through Jira.
Support production releases, issue resolution, and ongoing model performance monitoring.
Ensure adherence to enterprise governance standards, SDLC processes, and AML compliance policies.
Adopt the latest AI-based technologies to improve efficiency.