The role involves managing and coaching a team of analysts focused on data annotation and labeling to support machine learning initiatives. The lead will partner with engineering, analytics, and data science teams to develop strategies for optimizing training data. Responsibilities include collecting, curating, and validating data while developing taxonomies to train models. The position requires identifying patterns in conversational data using computational linguistic approaches. Additionally, the lead will collaborate with stakeholders to evaluate the quality of machine learning outputs. The role also entails contributing to a continuous learning environment by introducing new perspectives to the team.

The role requires at least six years of experience in developing machine learning solutions. Candidates must possess expertise in data technologies, including data warehousing, data processing, and data quality concepts, alongside familiarity with finance and banking products. Proficiency in Python and knowledge of machine learning paradigms, information retrieval, and prompt engineering are necessary. While a Bachelor's degree is the minimum requirement, a Master's or PhD in a related field is preferred.