The data team lead and growth hacker at BenQ. Leading the data team in business/tech for new system design and development.
Served as a co-founder of a delivery platform startup company and experience in writing a website, researching machine controller hardware, and some embedded system firmware.
Now specializing in deeply cultivating the AI field, using NLP, ML, DL, RL and various cutting-edge technologies.
Data Team Lead、Data Manager、Machine Learning Engineer、Deep Learning Engineer、Sr. Data Scientist
Data Team Lead、Data Manager、Machine Learning Analyst, Deep Learning Analyst, Growth Hacker, Sr. Data Scientist
Department structure :
- Leading Infrastructure team, Data Analysis team,和 Business Growth team
- 4 Engineers、4 Data Scientist & Analyst、2 Project manager
Business culture, Tech, Project Development :
- Building BenQ Data Driven culture and methodology for BUs and departments
- Push around 200+ projects such as A/B Testing and User behaviors analysis for BUs and departments to cultivate the data-driven culture
- Development market trend and sales forecasting modeling service for BUs with around 85% accuracy/R2 score
Team Building :
- Standard interview processing for Data Engineering, Data Science & Analysis, Project Manage, and Interviewed more than 100 Candidates, about 10 interviewed to the final interview.
- Data Team Bootstrap : Regular internal opening hours to establish new data products and algorithm establishment, process and cultural training from modeling to analysis reports
1. 【Products and Brand Key Words Predict Combination Model : All Product Line Volume Trend Prediction and Take Action/Generate the Insight】
Internal volume keywords trend predict model, insight generate and consultant subscribe business development.
2. 【Products Specs and Benefit Predict Combination Model : All Product Line Volume Trend Prediction and Take Action/Generate the Insight】
Internal data science specs combination & benefit predict model, insight generate and consultant subscribe business development.
3. 【GDPR Products Recommendation System】
Internal data science federated learning recommendation system model, Scenario recommendation with insight generate and consultant subscribe business development.
1. 【NLP-Business Metrics Combination : Improve the Metrics as Open Rate and Take Action/Generate the Insight】
Internal data science keywords search engine, Auto Tagging and recommendation system engine development.
2. 【NLP-Business Trend Combination : Finding the Keywords Field as Scenario Term like Indoor...etc and Take Action/Generate the Insight】
Analyze the international market of major consumer electronic products and develop business models based on different series of the products.
3. 【Product positioning STP model】
According to each segment market, for local or global e-commerce platforms such as Amazon, etc. Establish a keyword generation model, and predict product rating and the accuracy rate is 95%.
【Mobile game physique scoring analysis model】Result: The game analysis has been tested by senior game consultants in the industry, compared with the analysis of the system's automatic judgment, and the result coincides with 75%.
【Analysis Model of VIP Player Churn in Mobile Games】
Results: Based on the behavior of VIP users in the previous month, predict the person most likely to become churn, and verify the key reasons for churn, with an accuracy rate of 98%.
Use Python to develop Azure's speech recognition system.
This project is a cooperation project between Shanghai Bank and Microsoft Corporation. It will be exhibited together with Microsoft at the Nangang Exhibition Hall in August 2018.
The company’s main business is a university food delivery platform.
The highest order volume in a single day exceeds 6,000 orders, and the monthly turnover is 6 million Taiwan dollars Lay the company's main revenue foundation.
Studying in the AI and Information Security Group of the National Taiwan University Telecommunications Institute during holidays, ranked first in the first semester of the 109 school year, with an average GPA of 4.3/4.3
Credits have been completed, only graduation thesis left
Expected to graduate in 2021
National Taiwan University's GPA out of 4.3
GPA3.8 is converted to a percentage score of 85.67 points
Have a certain basic understanding of mechanical engineering and be able to use it in the field of work.
My advantage is: through the usual accumulation of industry observations and information collection, after finding possible market trends, follow the steps below to complete business development, market analysis, and market strategy.
Python, Data Analysis, Data Studio,
GCP,Tableau, MySQL and Excel
Good at algorithms: Random Forest, Logistic Regression, SVC, Yees Regression, Linear Regression, Xgboost, Ensemble learning System, k-means Clustering
Good at algorithms : CNN、RNN、Vgg16、
Commonly used open frameworks and platforms : Keras、 Pytorch、Mxnet、Tensorow2.0、 Azure
Good at algorithms : Bert, word2vec, TF-IDF
Good at algorithms : Q-Learning、DQN