Employed optimization techniques to efficiently schedule the dispatch of reserve power resources, thereby minimizing costs and environmental impacts

Implemented rigorous cybersecurity measures to safeguard data and control systems from potential threats and cyber attacks through a private API connection

Utilized secure cloud data capabilities in collaboration with Alibaba Cloud, authorized by the government, for research, development, and implementation of machine learning-driven innovations in power resource management

Developed new TypeScript functions with interface capabilities for local delivery and event management, facilitating the creation and joining of neighborhood groups based on shared interests

Contributed to product design and development discussions, focusing on real-time monitoring of model effectiveness and ongoing enhancement of product value

Enhanced user engagement within the app by integrating React Native, creating events calendar, bulletin board, and chat features

Utilized machine learning tools and technologies to model and analyze datasets across multiple dimensions and variables, as well as conducted extensive data mining and cleaning operations on over 100,000 records using Python

Analyzed churned user behavior to develop a model predicting churn risk, achieving a reduction from 10% to 7%; established an alert system to identify high-risk users and automatically engage them with incentives

Identified prospective user groups and analyzed the preferences of target demographics for new financial products to mitigate operational risks and strengthen current strategies