Mustafa Poonawala

Implemented K-means clustering and principal component analysis (PCA) for market segmentation to support targeted lending strategies and diversify portfolio risk. Visualized insights using Tableau for stakeholder reporting. Utilized Python (Pandas, Scikit-learn) and SQL to perform predictive modeling on borrower datasets, applying classification algorithms to assess applicant risk levels based on behavioral and financial performance metrics. Designed automated pipelines to extract, clean, and analyze financial data, enhancing analytical efficiency and decision support.

Created automated inventory management for data collection and order processing system through Amazon's SP-API, integrating into ETL workflows, resulting in a 40% reduction in data update times and enhancing data accuracy. Developed interactive dashboards using Looker Studio to visualize KPIs such as sales trends and inventory levels, leading to a 50% improvement in decision-making efficiency. Implemented process improvements to enhance operational efficiency, resulting in a 30% increase in productivity and a 25% reduction in order processing errors.

Led competitive analysis and user research to identify key customer pain points. Collaborated with design and engineering teams to implement UX enhancements using journey maps and personas to inform product requirements. Facilitated cross-functional collaboration with product, marketing, and development teams, ensuring seamless product execution in an Agile environment. Maintained JIRA boards and aligned feature delivery with stakeholder expectations. Automated product testing workflows using Selenium, reducing go-to-market time by 30% while ensuring product stability and user experience quality, saving 500 hours per month.