Career Profile
Passionate Data Science and Machine Learning Engineering professional with 3 years of experience in different retail sectors. complete numerous research and implement various algorithms on the recommendation system. Currently, focus on fintech with the government sector and deploy pipeline engineer for investment portfolio.
Hope to apply machine learning methods on more fields in the real-world.
Education
- Edge Intelligence, Spilt Learning, Brain-Inspired Computing
- Data Analysis, Machine Learning, Time Series Prediction
- Recommender System, Deep Learning
- Web Designing, Database Developement
Experiences
- Evaluate performance of different HPO toolkits
- Investigate and implement recent academic methodologies about feature selection
- build a back test pipeline model for the SP500 stock prediction with automatic feature selection and HPO
- Process and analyze time series data to create forecasting, evaluation and EDA reports for demo
- Deploy different machine learning model for various companies and provide advices
- Developed prediction module in Nike CTM sales prediction system
- Analyzed large sets of nationwide unstructured data and classified different patterns of stores
- Solved the holiday effect on abnormal sales and the promotion impact on certain products
- Researched L’oreal skin care sales data and extracted anomaly
- Assessed influences of live show on different types of L’oreal goods
- Improved the low accuracy of new L’oreal products with 12%
- Eliminated bias of weather effect on sales prediction for Starbucks in Shenzhen, China
- Deployed prediction models using for Starbucks’ daily material sales prediction
- Evaluate and improve the performance of Starbucks’s models affected by seasonality
Publications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 14th ACM Conference on Recommender Systems, 23-32