Weizheng Jiang

During my graduate studies, I actively participated in my professor's research projects, where I honed my data science skills through various tasks:

• Experimental Design and Data Collection: Designed experimental data collection protocols, gathered data through experiments, surveys, and focus groups.
• Data Cleaning and Annotation: Cleaned, organized, and annotated experimental data, ensuring high-quality datasets for analysis.
• Deep Learning Model Training: Utilized the prepared datasets to train deep learning models, enhancing their performance through iterative testing and validation.
• Academic Writing and Revision: Wrote academic papers and revised them based on feedback from peer reviewers, ensuring rigorous and impactful research outputs.
• Team Collaboration and Meetings: Actively participated in regular meetings with fellow researchers to discuss project updates, challenges, and lessons learned, fostering a collaborative research environment.
• Literature Review and Hypothesis Development: Conducted thorough literature reviews to support hypothesis development and identify gaps in existing knowledge.
• Research Material Organization: Organized research materials and maintained a well-ordered workspace to boost productivity.
• Effective Communication: Maintained open lines of communication with project leads and team members, facilitating effective collaboration on shared goals.

In my spare time during studies, I have been running an online bicycle accessories store on a Chinese e-commerce platform, achieving a total transaction volume of 640,000 RMB (approximately 150,000 NZD). Throughout this process, I have utilized data analysis to enhance the store's operations:

• Data Collection and Analysis: Regularly collected and analyzed data on product exposure, page views, orders, inquiries, transaction amounts, customer numbers, fan order ratio, new follower count, and conversion rates.
  Product Description Optimization: Optimized product descriptions and display methods based on data analysis to improve user conversion rates.
• User Demand Forecasting: Predicted user demand by analyzing sales data over specific periods and monitoring product discussion trends on social media and relevant communities. Adapted store operations to meet changing market conditions and customer needs while preserving overall business stability.
• Product Portfolio Design: Designed lead products and key promotional items based on profit margins and restocking cycles of different product categories to optimize inventory management.
• Enhancing User Engagement: Increased user retention by posting content based on follower preferences, repeat customer purchases, and user demand insights on the platform. Developed a loyal customer base with personalized assistance, attentive listening, and friendly interactions.
• Customer Satisfaction: Enhanced customer satisfaction by providing exceptional service and addressing customer inquiries or concerns promptly.