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

PhD candidate

2023 - Present
Zhejiang University
  • Edge Intelligence, Spilt Learning, Brain-Inspired Computing

Master of IT in Business

2021 - 2022
Singapore Management University
  • Data Analysis, Machine Learning, Time Series Prediction

Master of Management Science

2017 - 2019
Shanghai University of Finance and Economics
  • Recommender System, Deep Learning

Master of Computer Science

2013 - 2017
Shanghai University of Finance and Economics
  • Web Designing, Database Developement

Experiences

Machine Learning Engineer Intern

Jan 2022 - Jun 2022
Monetary Authority of Singapore, Singapore
  • 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

Algorithm Engineer

Mar 2019 - Mar 2021
Cardinal Operation, China
  • 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

  • DaisyRec 2.0 Benchmarking Recommendation for Rigorous Evaluation
  • Zhu Sun, Hui Fang, Jie Yang, Xinghua Qu, Hongyang Liu, Di Yu, Yew-Soon Ong, Jie Zhang
    IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison
  • Zhu Sun, Di Yu, Hui Fang, Jie Yang, Xinghua Qu, Jie Zhang, Cong Geng
    Proceedings of the 14th ACM Conference on Recommender Systems, 23-32

    Skills & Proficiency

    Python

    Torch

    Latex