Possess extensive experience in developing machine learning models, leveraging the capabilities of open-source tools and libraries such as scikit-learn, pandas, NumPy, XGBoost, and statsmodels to drive impactful model development and analysis.Actively engaged in a major financial institution's extensive model integration initiative, successfully transitioning risk models from SAS to Python. Constructed a Probability of Default (PD) model for a substantial portfolio, which included raw data preparation, model development, and performance testing. Managed an expansive dataset with over 15 million records and 350 variables during the model development phase. Additionally, contributed to the development of a non-retail Borrower Risk Rating (BRR) model by utilizing PySpark for data preparation tasks.Independently developed over 50 Python/PySpark-defined functions, significantly streamlining the risk modelling process and enhancing the efficiency and accuracy of the stress PD model.Collaborated closely with cross-functional teams to ensure strict adherence to banking industry standards and regulatory compliance.Proficient in utilizing cloud-based machine learning platforms, particularly AWS SageMaker, to enhance project efficiency and effectiveness.Contributed significantly to the creation of compelling PowerPoint presentations for client engagements, actively participating in client meetings to facilitate discussions and drive successful deal outcomes.Proven ability to thrive in high-pressure environments, adeptly managing stressful projects and navigating complex tasks to meet tight deadlines with precision and efficiency.

Applied advanced internal ratings-based (AIRB) methodologies, specializing in developing and implementing quantitative models, including Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD).Leveraged extensive domain knowledge and expertise to refine and enhance credit risk models, fostering a deep understanding of credit risk across various financial products and markets.

Assisted in developing an in-house cloud-based digital and automation solution for the financial services industry.Conducted case analysis for the project and authored competitor analysis and Statement of Work (SOW).Performed data preparation using MySQL Workbench and Excel.Utilized machine learning (ML) models to analyze customer business data and generate predictions of future business trends (task volumes) using Python.Conducted or participated in weekly meetings with the executive sponsor, product owner, and other consultants to provide progress updates and discuss any issues.

Software Development