• Build high performance ML models that improve credit predictability and customer outcomes. • Build end-to-end machine learning pipelines including data extraction, feature engineering, model building, performance evaluation, and implementation. • Build end-to-end model monitoring pipelines to monitor model performance, including model stability, drift detection, and performance diagnostics. • Develop and own reporting pipelines that support recurring risk reporting, monitoring, and ad-hoc analysis • Explore rich data assets to uncover trends, identify key risk drivers, and generate insights. • Define and monitor data quality metrics (completeness, accuracy, timeliness, consistency) • Manage full modeling lifecycle: model design, data preparation and exploration, feature engineering, model development, evaluation, deployment, monitoring and documentation. • Design experiments and challenger models to test new ideas and drive innovation. • Contribute to audit-ready documentation and support model validation and governance processes. • Leverage modern AI technology and tools to improve workflow, enhance modeling performance, establish and promote best practices. • Work cross-functionally to ensure models are explainable, transparent, and aligned with business goals and regulatory expectations. • Translate analytical findings into recommendations for Strategy, Pricing, and other business decisions. • Build pipelines that are scalable, automated, and well-documented.