Evaluating the Impact of Schema Evolution on Machine Learning Pipeline Stability • Designed a reproducible experimental framework to test how schema changes affect ML pipeline stability and prediction behavior. • Evaluated 700 benchmark tasks and 8,400 pipeline executions under controlled schema evolution scenarios. • Built Python-based workflows for preprocessing, validation, alignment, and prediction drift analysis. • Applied metrics including RMSE, KS statistic, Spearman correlation, prediction agreement, and Jensen–Shannon divergence.

• Led initiatives to improve internal workflows and reporting systems across departments • Identified opportunities to streamline processes and improve operational efficiency through digital solutions • Worked with technical and non-technical stakeholders to translate business needs into practical system improvements • Supported implementation of data-driven solutions and workflow automation

• Worked with database-driven business systems across warehouse, finance, and procurement operations • Developed SQL-based reports and data workflows to support operational decision-making • Supported integration of data across systems and improved consistency of business data • Automated reporting processes and reduced manual effort through scripting and structured workflows • Collaborated with stakeholders to identify opportunities for process optimization and system improvements