The role involves driving the end-to-end lifecycle of data and artificial intelligence products within a financial context. The individual will develop and implement machine learning models to uncover patterns and insights related to credit risk. Responsibilities include optimizing model performance, designing automated monitoring solutions, and ensuring the explainability of analytical models. The position requires developing scalable data science frameworks and integrating algorithms into production-grade applications. Furthermore, the role involves collaborating with cross-functional teams to manage the product lifecycle from ideation to post-launch monitoring. Adherence to strict regulatory standards and responsible development principles is a core component of the work.

Candidates must possess a Master's degree in Data Science, Computer Science, or a related quantitative field. The role requires hands-on experience in building and deploying machine learning solutions in production environments. Proficiency in Python and specific machine learning libraries is mandatory, alongside expertise in MLOps and API development using FastAPI. Applicants should demonstrate strong knowledge of quantitative methods, statistics, and probability theory.