Liming Liu

●​ Monitored and analyzed DSP (Delivery Service Partner) performance data, identifying operational issues and supporting daily site management and coordination ●​ Consolidated and maintained operational datasets, producing structured daily and weekly reports to support decision-making ●​ Collaborated with the quality control team to maintain and optimize dashboards, improving visibility of key performance metrics ●​ Streamlined workflows by developing and implementing two SOPs, enhancing consistency and execution efficiency ●​ Built an automation bot using Lark's multi-dimensional tables wizard to improve data tracking and reporting efficiency ●​ Reduced total DSP issue volume by 70% within 1 month through data-driven analysis and process improvements

●​ Performed standardized interviews with research samples, adhering to structured protocols to maintain data consistency and reliability ●​ Cleaned, processed, and summarized datasets using Python, transforming raw data into analysis-ready formats to support research insights ●​ Conducted data collection using the Big Apple research tool, ensuring accurate and systematic acquisition of study data

●​ Collaborated with Warehouse supervisors to collect and analyze operational data, supporting process improvement initiatives that enhanced efficiency ●​ Applied foundational Lean principles to assess and propose improvements in workflow, layout, and labor allocation, leading to more streamlined operations ●​ Assisted in the review, development, and standardization of SOPs and operational training, improving consistency and efficiency in training processes, which led to a 93% reduction in misrate.

●​ Created and enhanced MySQL query prompts for high-volume data to increase query speed and improve code robustness, resulting in more efficient data retrieval ●​ Created data model visualizations and built dashboards using Holistic BI, improving data accessibility for team members ●​ Generated daily and business insight reports and presented findings to the supervisor, aiding in strategic decision-making

●​ Researched, cleaned, and managed large-scale datasets to ensure high quality and accuracy, supporting effective decision-making ●​ Developed and executed data queries and integration using Cypher, optimizing the association and processing of multi-source data ●​ Leveraged AI techniques for advanced data visualization, transforming complex data into actionable insights

●​ Collaborated with team members on data collection by implementing web scraping techniques with Scrapy and managing structured data extraction using Oracle SQL, which streamlined the data collection process ●​ Provided model development and optimization support, leveraging machine learning algorithms in sci-kit-learn and PyTorch for performance tuning, resulting in a 10% improvement in overall model performance ●​ Developed visualizations using Matplotlib to communicate key findings and model outputs, enhancing team understanding and decision-making

●​ Compiled and cleaned data sets using Excel and MySQL, leading to an increase in data completeness for subsequent analysis ●​ Created interactive Power BI dashboards, highlighting key metrics, which were instrumental for business analysts in trend identification, increasing data accessibility for teams ●​ Authored comprehensive test cases in Python for model validation, resulting in an improvement in model reliability

●​ Constructed and maintained databases based on Microsoft Access, leading to an increase in data accessibility for internal users ●​ Assisted in front-end website development and collaborated with cross-functional teams to enhance user experience ●​ Identified and resolved data integrity issues, ensuring consistency and reliability of datasets

●​ Relevant Coursework: Data Analysis, Stochastic Modeling, Optimization, Business Data Analysis

●​ Relevant Coursework: Data Mining, AI Machine Learning, Database Systems, Six Sigma