Job Title: AML Data Scientist

As an AML Data Scientist, you will play a critical role in combating financial crimes by leveraging your expertise in data science, machine learning, and advanced analytics. Your primary focus will be on designing and enhancing AML detection models, analyzing customer behaviors, and ensuring compliance with regulatory standards. This is an exciting opportunity to work in a dynamic, fast-paced environment where your contributions will directly impact the prevention of money laundering and other financial crimes.

Key Responsibilities:

• Develop and implement machine learning models and advanced analytics solutions to detect and prevent money laundering, terrorist financing, and other financial crimes.
• Collaborate with Financial Crime Compliance (FCC) teams to understand regulatory requirements and translate them into data-driven solutions.
• Analyze large-scale transactional and customer data to identify unusual patterns, anomalies, and suspicious activities.
• Optimize and enhance existing AML models, including transaction monitoring, customer risk scoring, and sanctions screening systems.
• Conduct exploratory data analysis (EDA) to uncover insights and improve detection capabilities.
• Partner with engineering and data teams to ensure the scalability and seamless integration of AML models into production systems.
• Monitor and evaluate the performance of AML models to ensure they remain effective and compliant with evolving regulatory standards.
• Create dashboards, reports, and visualizations to effectively communicate findings and insights to stakeholders, including compliance officers and senior management.
• Stay updated on the latest advancements in data science, machine learning, and AML regulations to continuously refine methodologies.
• Support audits and regulatory inquiries by providing data-driven evidence and comprehensive documentation.

Qualifications:

Education and Experience:

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
• 7+ years of experience in data science, machine learning, or analytics, preferably in the financial services or banking sector.
• Proven experience in AML, fraud detection, or financial crime prevention is essential.

Technical Skills:

• Proficiency in programming languages such as Python, R, or Scala.
• Strong expertise in machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Experience with big data technologies (e.g., Hadoop, Spark) and cloud platforms (e.g., AWS, Azure, GCP).
• Advanced SQL skills for data extraction, manipulation, and analysis.
• Familiarity with graph analytics and network analysis for link-based detection of suspicious activities.
• Knowledge of AML-related tools and platforms (e.g., Actimize, SAS AML, or similar) is a plus.

Analytical and Domain Knowledge:

• Comprehensive understanding of AML regulations, including FATF recommendations, KYC, and customer due diligence processes.
• Experience working with unstructured data and developing Natural Language Processing (NLP)-based solutions for text-based analysis, such as sanctions screening.
• Expertise in building and evaluating predictive models, anomaly detection algorithms, and segmentation techniques.
• Strong analytical skills with the ability to interpret and explain complex data patterns.

Soft Skills:

• Excellent communication and presentation skills, with the ability to convey technical findings to non-technical stakeholders effectively.
• Strong collaboration skills and the ability to work efficiently in cross-functional teams.
• Exceptional attention to detail, with a commitment to maintaining high standards of accuracy and integrity.

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