Plan and lead data science and machine learning projects for financial applications. Design, deploy, validate, monitor, and improve scalable models for transaction classification, temporal analysis, risk modeling, and structured or unstructured data. Develop innovative solutions using machine learning and data engineering best practices, identify team resource needs, and communicate technical findings, strategy, and roadmaps to business leaders, clients, and stakeholders.

Develops and deploys machine learning and statistical models for financial applications, including transaction classification, temporal analysis, entity resolution, and risk modeling. Responsibilities include analyzing large structured and unstructured datasets, validating and monitoring model performance, creating scalable solutions, communicating findings to business leaders and clients, and applying NLP, deep learning, and data science best practices.