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

comprehensive health care coverage and emotional well-being support
market-leading retirement
generous paid time off and parental leave
charitable giving employee match program
educational assistance including student loan repayment, tuition reimbursement, and learning resources to develop your career

Principal Data Science Engineer – Financial Crimes

Key skills