Jin Ji Email and Phone Number

•Product Research and Design: Launched an AI data analysis agent with a 50% internal usage rate; designed prototype diagrams and interactive interfaces for the company’s LLM product, enhancing internal data-driven decision-making.•Model Evaluation and Optimization: Evaluated and compared over 30 LLM models across 10+ dimensions, including financial planning and market analysis; fine-tuned training and optimized the company’s LLM model for financial data analysis, leading to a 60% improvement in model instruction compliance.•Product Operation and Promotion: Implemented beta incentives, boosting DAU and WAU by 50%; co-authored a high-impact technology report, increasing official social media followers by 300%.

•Conducted data analysis in the field of anti-cheating, utilizing a data-driven approach. Monitored platform dashboards to promptly raise alerts for data anomalies. Primarily focused on user status, gender, active days, and registration channels. Regularly scrutinized indicators such as DAU, behavior UV, behavior penetration rate, and behavior PV. •Analyzed daily dataset of spam users, incorporating SQL-derived user behavior data from the previous day. Identified abstract features of anomalies, such as similar user avatars, identical names, and device counts surpassing. Develop and implement strategies for relative users to improve the social environment and user experiences in the platform.

•Responsible for predicting the results of football matches through data analysis and uploading the analysis report to the official blog to help the company get more users•Performed exploratory data analysis based on the 2020-2023 Premier League match data and conducted the basic feature engineering•Applied Logistic Regression, Support Vector Machine, and Random Forest to predict the winners of the matches•Constructed new features reflecting changes in team strength by ELO Ranking and Colley Ranking, improving the prediction accuracy of Logistic Regression and Random Forest models

•Conducted equity research in the textile and apparel industry, including analyzing data from the Wind platform and completing two in-depth research reports on leading companies in the industry from the perspectives of business model, industry trends, and operation condition•Completed a research report on the urban outdoor sports industry, including future development, competitive barriers, and growth drivers; concluded that the industry has a large market space, but leading companies have already formed barriers; picked out high-potential companies in different market segments