"Strong proficiency in Python for machine learning model development, data analysis, and statistical analysis. Extensive experience with SQL for building and optimizing data pipelines and feature stores. Proven experience in developing credit risk models, fraud detection models, and recommendation systems within the lending or financial services industry. Hands-on experience with graph databases (e.g., Neo4j) for fraud detection and relationship-based data modeling. Experience with Amazon SageMaker for deploying machine learning models in a scalable, production-ready environment. Familiarity with Amazon Redshift or other data warehousing solutions for high-performance data processing. Exposure to Amazon Personalize or similar recommendation engine tools is a plus."