The role involves developing, enhancing, and performing statistical testing on AI, machine learning, and generative AI models within an anti-money laundering transaction monitoring system. The professional manages these models throughout their entire life cycle, including performance evaluation and annual reviews. Responsibilities include translating business requests into data criteria, conducting operational research, and producing analytics to manage risk. The individual creates detailed technical documentation to meet regulatory guidelines and communicates findings to diverse audiences. Additionally, the role requires providing effective challenges to model assumptions and data quality while contributing to cross-functional strategic initiatives.

Candidates must possess at least six years of experience in a quantitative risk management role within a financial institution, specifically in model development or validation. A Master’s or University degree in statistics, computer science, quantitative economics, mathematics, or a related field is required. Necessary technical competencies include proficiency in Python, SQL, and Big Data solutions, alongside a strong background in data analysis and processing. Furthermore, the role requires deep knowledge of anti-money laundering typologies, detection scenarios, and regulatory expectations for AI and machine learning models.