Principal Data Science Engineer – Financial Crimes

Responsibilities

• Collaborate with team members and compliance partners to understand AML typologies and red flags we must detect
• Assist in building detection models and features using SQL, Python, DBT (Data Build Tool), and Snowflake
• Develop detection models using both rules‑based and machine learning algorithms on customer, account, and transaction data
• Apply machine learning and AI techniques to enhance suspicious activity detection by analyzing and identifying appropriate target data
• Monitor and optimize model performance using proper ML Operations tools
• Help drive AI use cases for investigative workflows, including integration in alert management systems, narrative generation, and straight‑through SAR filing
• Champion best practices for CI/CD, robust automated testing, model performance, and production monitoring
• Provide technical leadership, mentoring and training to other team members through code reviews, collaboration, and educational presentations
• Explore new technologies (e.g., anomaly detection, graph analytics, predictive modeling) and determine their applicability to the team’s use cases; orchestrate the adoption of such technologies and trends where appropriate

• Bachelor’s degree in Computer Science or equivalent technical discipline
• 6+ years of experience in software or data engineering, including leading and delivering complex projects
• Strong proficiency in Python or at least one object‑oriented programming language (e.g., Java) with a focus on writing clean, modular, and testable code
• Strong experience querying relational databases (e.g., Oracle, Snowflake) and working with non‑relational databases (e.g., MongoDB)
• Hands‑on experience with machine learning algorithms, including decision trees, neural networks, regression models, clustering, and anomaly detection
• Prior experience working with customer and transactional data in the fraud or AML space
• Understanding blockchain technologies; prior experience in cryptocurrency monitoring is a plus
• Experience with dbt (data build tool) for data transformation and pipeline development
• Prior experience developing solutions using large language models (LLMs), including Retrieval‑Augmented Generation (RAG) for information retrieval and workflow automation
• Certifications such as CAMS (Certified Anti‑Money Laundering Specialist) or CFE (Certified Fraud Examiner) are desirable