In my role as a Data Scientist, I led significant machine learning initiatives, applied strategic data-driven approaches, and provided mentorship within a high-performing analytics team. My contributions were key to advancing the bank’s data capabilities, impacting customer engagement, operational efficiency, and sales.• Spearheaded the development and deployment of a wide range of machine learning models. This included supervised learning methods for credit risk assessment and lead scoring, as well as unsupervised learning methods for client clustering and product recommendations. My work with custom neural networks using PyTorch substantially enhanced client evaluation and risk management accuracy.• Leveraged Python ML technologies (Scikit-learn, Pandas, Numpy) and Power BI for advanced data visualization, enabling comprehensive analysis and actionable insights from complex datasets.• Implemented a dual-layer client selection strategy for loan processing, integrating lead scoring with risk assessment, which increased the cold sales rate by over 4% and improved loan processing efficiency.• Championed the adoption of SAS for data analysis, which led to a reduction in machine learning project deployment times by 50%, significantly boosting operational efficiency.• My machine learning models were instrumental in increasing the department’s total deposit sales share by 15% from 2021 to 2022, underscoring the impactful role of data science in the bank’s financial success.• Acknowledged as one of the bank’s leading data scientists, I played a pivotal role in team leadership and mentoring junior data scientists, while also contributing to key strategic decisions like vendor selection for database management systems and other services.