Hamza Hamayun

Client: Major Shopping & entertainment hub - Identified participant segments through K-means clustering of loyalty program data - Improved engagement by uncovering store co-visitation patterns through association mining, informed targeted marketing Client: Non Profit Organization - Analyzed finance data for pre and post-execution of financial literacy program through K-means clustering - Grew program savings by 31% by identifying key metrics through panel regression and analysis of participant segments Client: Minnesota High School - Analyzed K-12 and post-secondary student data in a top 10 Minnesota school district, employing Python-based Support Vector Machine modeling to identify key factors influencing post-secondary success and revealing disparities in the effectiveness of early college programs across student groups

- Led a team of graduate student volunteers in planning and executing a 2-day analytics-focused event at the Carlson School. - Conceptualized and implemented a comprehensive strategy for a Hackathon, featuring 130+ students and drawing participation from 20+ universities nationwide.

- Increased delivery driver reactivation by 20% by creating weighted means scoring model and tailored reactivation strategies - Implemented driver tiers and adjusted weekly incentives based on performance metrics, leading to 3% increase in order growth - Achieved reduction in reinstatement time of delivery drivers, reducing it from 48 hours to real-time by developing a dashboard on Tableau to automate the manual reactivation process

- Automated 72 hr+ manual price adjustment process with real-time automation through a Google Looker Studio dashboard - Integrated live market prices through webscraping and SQL, eliminating manual approval and ensuring accurate pricing adjustments for vendors on the platform - Boosted vendor experience score by 5% with a logistics dashboard on Tableau, leveraging data analysis to optimize order stacking, delivery times, and product-level strategies, such as optimizing stack percentages at the product level

- Enhanced accuracy by 47% on AWS forecasting, restructured existing demand forecasting model by adding relevant features - Reduced product cannibalization using XGBoost models with 85% accuracy rate, forecasting customer type and facilitating strategic revisions to marketing approaches

Worked with Assistant Professor of Economics Adeel Tariq, to analyze economic data and make a Linear Regression Model which explains the changes in total factor productivity of Pakistan over the past five decades.

- Predicted the electricity tariffs for tube wells for the next 5 years in Lahore region by designing a regression model using time series data to develop a strategy to control costs. - Cleaned and parsed granular data to perform exploratory data analysis and to capture and eliminate inconsistencies

Event Head for one of the events of PSIFI- the biggest Science Olympiad in Pakistan. - Restructured the format for the event to enable a better learning experience for the participants. - Mobilized 10+ team members to manage the event with more than 300 participant. - Effectively delegated tasks to ensure a smooth progression of the event.

Merit Scholarship Recipient Relevant courses: - Predictive Analytics - Big Data Analytics - Causal inference via econometrics and experimentation - Statistics for Data Science - Python Programming for Data Science - SQL - Advance Business Analytics - Optimization