Alzhazira Zhuman

Analyzed the end-to-end vehicle pre-order fulfillment process using VSM to identify bottlenecks causing loan approval expirations. Designed "As-Is" and "To-Be" process maps via BPMN and UML, projecting a 40% reduction in document re-collection cycles and minimizing customer onboarding delays. Applied TOGAF architecture principles to streamline cross-functional workflows between sales, logistics, and partner banks.

Formulated data-driven solutions to optimize pricing strategies by analyzing core vehicle features across a dataset of 7,000+ listings. Developed a predictive machine learning model to estimate optimal car prices, achieving 92% model accuracy in price forecasting. Conducted feature importance analysis to identify top variables influencing vehicle depreciation and market value.

Gathered and preprocessed a comprehensive real estate dataset of 18,000+ entries, handling missing values and anomalous data points. Implemented and evaluated Linear and Logistic Regression models to automate property valuation based on structural and regional factors. Designed interactive data visualizations using Matplotlib and Seaborn to communicate hidden market trends and analytical insights.

Scraped and aggregated a live dataset of 1,272 IT job vacancies in Almaty from hh.kz to analyze market demands and entry-level barriers. Conducted a comprehensive market analysis focusing on junior and internship opportunities, discovering key trends regarding experience requirements across different IT tracks. Designed interactive analytical dashboards and visualizations to map out the most high-demand tech stacks and entry points for graduates.