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On
4-6 years
6-10 years
10-15 years
More than 15 years
Past
博士後研究員 @洛桑大學神經發育疾病實驗室2023 ~ 2023
・
Data Scientist, Data Analyst, Machine Learning Engineer
Within one month
Unemployed
・
Ready to interview
Full-time / Interested in working remotely
4-6 years
洛桑聯邦理工學院(EPFL)
・
神經科學
Past
Service Provider @Taron Solutions Limited2023 ~ 2023
・
AI工程師、機器學習工程師、電腦視覺工程師、資料科學家、Machine Learning Engineer、Computer Vision Engineer、Data Scientist
Within one month
李慕全(MuChuan Li) 畢業於國立臺北科技大學資工所，研究領域為深度學習、電腦視覺、及影像處理。在學期間致力於應用電腦視覺技術解決交通問題，擁有多項產學合作的專案開發經驗，亦在電腦視覺領域中發表過多篇學術論文，主要研究主題包含物
Unemployed
・
Ready to interview
Full-time / Interested in working remotely
4-6 years
國立臺北科技大學
・
資訊工程AI工程師、機器學習工程師、深度學習工程師、資料科學家、Machine Learning Engineer、Deep Learning Engineer、Data Scientist
Within one month
Employed
・
Ready to interview
Full-time / Interested in working remotely
4-6 years
國立政治大學（National Chengchi University）
・
資訊科學系Past
Data Engineer @Rooit Inc. (XO App)2023 ~ 2023
・
AI工程師、機器學習工程師、深度學習工程師、資料科學家、Machine Learning Engineer、Deep Learning Engineer、Data Scientist
Within one month
Unemployed
・
Ready to interview
Full-time / Interested in working remotely
6-10 years
中國醫藥大學(China Medical University)
・
臨床醫學研究所
智慧製造全端開發工程師 @聯華電子股份有限公司2022 ~ Present
・
AI工程師、機器學習工程師、深度學習工程師、影像演算法工程師、資料科學家、Machine Learning Engineer、Deep Learning Engineer、Data Scientist
Within one month
潘揚燊 ㄕㄣ Shen Pan Kaohsiung City，Taiwan • [email protected] 希望職務：人工智慧、機器視覺應用開發工程師 現任 : 聯華電子 RPA 平台全端開發工程師 您好，我是潘揚燊，目前任職於 聯華電子 ， 擔任 智慧製造 全端開發工程師 ， 畢業於元智大學工業工程與管理學系研
Employed
・
Ready to interview
Full-time / Interested in working remotely
4-6 years
元智大學
・
工業工程與管理學系所
Senior engineer @Chicony Electronics Co, Ltd.2018 ~ Present
・
全端工程師、後端工程師、前端工程師、軟體專案主管、AI工程師、機器學習工程師、深度學習工程師、資料科學家、Machine Learning Engineer、Deep Learning Engineer、Data Scientist
Within one month
Nelson Chen Senior engineer Dedicated Software Engineer with 6+ Years of Experience Senior software engineer specializing in web page development and deep learning. Proficient with machine learning technologies, such as TensorFlow, Numpy, etc. Experience Senior engineer • Chicony Electronics Co, Ltd. ．Build an Auto-Encoder AI model for defective detection. ．Build an object detection model for detecting car types. ．Developed a Front-End and Back-End website for data analysis. ．Manage the production process and make it automated production. NovPresent Software engineer • Teco image systems co. ltd ．Developed and maintained MFP driver
Employed
・
Ready to interview
Full-time / Interested in working remotely
6-10 years
National Taiwan Ocean University
・
Computer science and engineering
Algorithm Research & Development @適着三維科技股份有限公司 TG3D Studio Inc.2021 ~ Present
・
Software Engineer
Within one month
Patrick Hsu AI Research & Development As a seasoned AI engineer with six years of experience, I specialize in computer vision, 3D body model reconstruction, generative AI, and possessing some knowledge in natural language processing (NLP). | New Taipei City, [email protected] Work Experience (6 years) Algorithm Research & Design• TG3D Studio MayPresent A skilled engineer specialized in computer vision and generative AI with experience in developing and training AI models for digital fashion applications. Body AI: Virtual Try On Integrated cutting-edge technologies such as Stable Diffusion, ControlNet, and Prompt Engineering to create a sophisticated system for
Employed
・
Ready to interview
Full-time / Interested in working remotely
4-6 years
國立台灣大學
・
生物產業機電工程所
Firmware Engineer @Lanner Electronics Inc.2021 ~ Present
・
Firmware Engineer, Firmware Developer, Embedded Software Engineer
Within one month
and a Bachelor’s degree in Electronic Engineering from the NKFUST. Proficient in firmware development using C, with hands-on experience in Embedded Linux System, MCU and Linux System, such as the OOB solution(on NUC980), Platform software Package, and FreeRTOS(on STM32) . comprehended to Python, TensorFlow, and machine learning concepts during university studies. Furthermore, I have proven track record of independently tackling challenging technical projects and embracing new technologies. I am passionate about continuous learning and self-improvement, as evidenced by my engagement in OOB solution, FreeRTOS, Yocto Linux kernel and machine learning with Python
Employed
・
Ready to interview
Full-time / Interested in working remotely
4-6 years
國立高雄科技大學（原國立高雄第一科技大學）
・
電子工程
Past
Senior Software Engineer @上海赛厨网络科技有限公司2016 ~ 2024
・
React Native Mobile App Developer
Within one month
张云(Cloud Zhang) Senior Software Engineer | React Native | Android - 7 years of development experience, including 4 years in Android development, 4 years in React Native app development, and 2 years in React web development - Fast-learning new technologies, quickly diving into the project Shanghai, China | [email protected] ｜Experiences Senior Software Engineer • SIDECHEF INC. JanuaryJanuaryReact Native App development and performance optimization - Web applications development - Android SDK development - Integrate third-party SDK for both App & Web - Machine Learning development on ETA model written in Python Software Engineer • SIDECHEF INC. AprilJanuaryCollaborated with team to migrate Android/
Unemployed
・
Ready to interview
Full-time / Interested in working remotely
6-10 years
赣南师范大学
・
计算机科学与技术
Past
Lead Data Scientist / Senior Data Scientist @Vinnovation Network 維諾森資訊科技2022 ~ 2023
・
資料科學家、資料科學工程師、機器學習工程師
Within one month
Shih-Wen Tsou - With more than 5 years of experience in Data Analysis, Machine Learning and Deep Learning, familiar with Modeling, Data Analysis, Image Processing, Machine Learning, and Deep Learning. Taipei City, Taiwan WORK EXPERIENCE Lead Data Scientist / Full Stack Data Scientist, Vinnovation Network, Taipei, Taiwan Data Engineering / Data Analysis Spearheaded the development of a fully automated data integration pipeline that aggregated diverse data sets into a S3 Data Lake. Successfully integrated a range of data sources, including real-time data feeds from AWS Redshift and DocumentDB, as well as batch processes to import traditional CSV
Unemployed
・
Ready to interview
Full-time / Interested in working remotely
4-6 years
台灣大學
・
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Definition of Reputation Credits
Technical Skills
Specialized knowledge and expertise within the profession (e.g. familiar with SEO and use of related tools).
Problem-Solving
Ability to identify, analyze, and prepare solutions to problems.
Adaptability
Ability to navigate unexpected situations; and keep up with shifting priorities, projects, clients, and technology.
Communication
Ability to convey information effectively and is willing to give and receive feedback.
Time Management
Ability to prioritize tasks based on importance; and have them completed within the assigned timeline.
Teamwork
Ability to work cooperatively, communicate effectively, and anticipate each other's demands, resulting in coordinated collective action.
Leadership
Ability to coach, guide, and inspire a team to achieve a shared goal or outcome effectively.
Professional Background
Current status
Employed
Job Search Progress
Open to opportunities
Professions
Data Scientist, Machine Learning Engineer
Fields of Employment
Banking, Artificial Intelligence / Machine Learning, AdTech / MarTech
Work experience
4-6 years
Management
None
Skills
Languages
English
・
Fluent
Job search preferences
Positions
AI工程師、機器學習工程師、深度學習工程師、資料科學家、Machine Learning Engineer、Deep Learning Engineer、Data Scientist
Job types
Full-time
Locations
台灣台北, 台灣新北市
Remote
Interested in working remotely
Freelance
Yes, I freelance in my spare time
Educations
School
政治大學
Major
統計
Education
National Chenchi University, MS, Statistics, 2015 – 2017
- GPA : 3.84 / 4.0
- Master Thesis: Entropy Based Feature Selection, Professor Pei-Ting, Chou
- Objective: Build a similarity matrix based on Mutual Entropy under Hierarchical Clustering. Afterwards, select clustered features as the final selection.
- Compare the model with other feature selection methods like RF, Lasso, F-score.
National Chen-Kung University, BS, Mathematics, 2011 – 2015
Skills
Programing
- Python
- Scala
- R
- MSSQL
Data-related Tools
- Tensorflow (Keras)
- PyTorch
- Spark
- Docker
- Scikit-Learn
- Pandas
Cloud Platform
- AWS
- GCP
Language
- English: TOEFL 98 / 120
Work Experience
CTBC Bank, Model Development Department, Data Scientist
2021.12 – present
- About the department:
- Responsible for developing models related to bank recommendations and risks, including projects such as coupon recommendations, account opening marketing lists, and fraud detection.
- Job responsibilities:
- Throughout the entire project lifecycle, my primary responsibilities included model design, model training, end-to-end process development, feature design, performance tracking, and method research.
Fraud Alert Project
- Objective:
- Predicting potential fraudulent accounts based on transaction data, restricting transactions in advance to prevent harm.
- Responsibilities/Achievements:
- Development and deployment of credit card and financial features.
- Managing the data flow process from receiving variables to model predictions, identifying risk factors, and updating alert lists.
- Implemented Autoencoder + contrastive learning to achieve a 1.81% improvement in model effectiveness.
Coupon Recommendation
- Objective:
- Personalized coupon recommendations for mobile banking users to increase click-through rates and redemption rates.
- Responsibilities/Achievements:
- Utilized multi-task learning to simultaneously predict click-through behavior and coupon redemptions, resulting in a 14% increase in click-through rate and a 74% increase in redemption rate.
- Created performance tracking reports to monitor online model performance and provide insights to Business Units.
Financial Product Recommendations
- Objective:
- Tailored financial product recommendations for mobile banking users to enhance click-through rates without compromising conversion rates.
- Responsibilities/Achievements:
- Applied multi-task learning to jointly learn click-through and conversion behaviors, fine-tuned model architecture, achieving a 90% outperformance against competitor models in online testing.
Marketing List for Digital Savings Accounts
- Objective:
- Optimized conversion rates for marketing lists related to digital savings accounts
- Responsibilities/Achievements:
- successfully raising conversion rates from 0.23% to 1.16%
Work Experience
CLICKFORCE, Data Engineer Supervisor, 2020.1 – 2021.11
- About the company:
- As a top domestic digital advertisement company, CLICKFORCE cooperates with over 900 web media and over 400 mobile media to build a huge advertising environment. CLICKFORCE considers data-driven solution as the core concept of the company, and dedicates to help advertisers to achieve their commercial goals.
- At 2020, CLICKFORCE won 2 awards at Agency & Advertiser of the Year.
- Successfully acquire the exclusive advertising agency qualification for Tokyo 2020 Olympics in Taiwan.
- Job responsibilities:
- Optimize ad performance from all aspects, including the system, target audience tags, etc.
- Do researches for new ML model (recommender model, NLP model) or architecture which is suitable for our system.
- Develop data-related products or projects.
- Analyze data to help improve our system or inspect whether the demands from business side is doable.
Real-time AD Recommender System
- Objective:
- Building a real-time ad recommender system to upgrade our ad server and get better performance.
- Responsibilities:
- Figure out what kind of recommender system components that is suitable for our ad system.
- Build a tower-like and feature-cross model refer to other famous recommender system model.
- Responsible for system engineering, which includes data preprocessing, embedding generates, memory cache, cold start, model API, etc.
Interest Tags
- Objective:
- Build interest tags for ads to help ad optimizers choose their target audience.
- Responsibilities:
- Create the features from what articles they saw, what website they viewed, and what ads they interacted.
- Deal with 20 million rows data and 120 million inference samples.
- Build ML model to predict each user's behavior on certain ads.
- Using Spark through AWS EMR to accelerate the speed of producing tags.
- Achievements:
- Raise CTR performance up to 200-300% of the original tags depends on different tags, and gain more impression while maintain better performance.
- After accomplishing this project, we terminated the cost on purchasing interest tags from other company, and successfully turned the original cost into revenue by providing profitable data.
First Party Cookie Mapping
- Objective:
- Deal with the Google 3rd party Cookie issue, figure out a method to map numerous 1st party Cookies to a user.
- Responsibility:
- Transform this problem into a ML mission. Design the label of the data, figure out what feature we can get or produce and whether the feature is useful for the goal.
- Apply XGboost on this mission.
- Build a small test to prove this method works.
- Achievement:
- 70% of precision.
- One of the solution of our company while the cancelation of 3rd party Cookie happen.
Invoice Data Application
- Objective:
- Develop invoice data application.
- Responsibility:
- Responsible for fine-tuning BERT to predict category for each product.
- Produce invoice data report to brands or business unit. It demonstrates the sales volume across different channel, what kind of products are frequently bought together, and also shows comparison of target brand to the other brands.
- Achievements:
- Produce an invoice data report product.
- Produce invoice tags for ad system.
Other Experience
E.Sun AI 2020 Summer Competition, 2020.7 – 2020.8
- Objective:
- Extract names of money laundering suspects from an article.
- Responsibilities:
- Crawl the articles from different media, and parse them by using Selenium, Requests, and Beautiful Soup.
- Construct 2-step model: First, identify whether the article is related to money laundering. Second, extract the suspects' names.
- Build model serving API by Tensorflow Serving.
- Build REST API for preprocessing request data and return the prediction.
- Achievement:
- 23rd place among 409 teams.
Youtube Data-Driven Marketing System, Institute for Information Industry, 2019.8 – 2019.11
- Objectives:
- Use the title and the description of videos to automatically classify videos.
- Use the title and the description of videos to identify whether a video is sponsored.
- Give suggestions for Youtubers or companies who desire to sponsor in a video based on data analysis.
- Responsibilities:
- Apply Google API and write Python functions to get structured raw data.
- Train word vectors using Gensim based on Wiki's open data.
- Use the frequency of each sentence as a criteria to eliminate useless words.
- Tune LSTM, Conv1D, BERT on the NLP mission.
- Use EDA methods to see the insights of the data under different classes and different sponsored status.
- Achievement:
- 71% accuracy in classifying video’s type.
- 89% accuracy in detecting sponsored content.
E.Sun Real Estate Price Prediction Competition, 2019.7 – 2019.8
- Objective:
- Use the real estate training data to build a model and predict the real estate price within 10% residual.
- Responsibilities:
- Apply XGBoost, LGBM and other ML models to train the model.
- Collect the outputs as new features from each ML model and add them into the original data set to enhance the performance of the final model.
- Achievement:
- 150th place out of 1200 teams.
KKTV Data Game，2017.5 – 2017.6
- Objective:
- Predict the next video a user watch in the next time interval.
- Responsibilities:
- Extract different features from raw data, such as the latest video, the video which got the longest viewing time, the video which got the largest number of viewing.
- Use the user viewing data to construct a similarity matrix of each video as additional features.
- Achievement:
- 10th place out of 50 teams.
MRT Open Data Competition, 2017.4 – 2017.5
- Objective:
- Study the changes of passenger volume of MRT by surrounding geometric data.
- Responsibilities:
- Apply bisection method to build the edges between MRT stations.
- Combine other geometric data based on these borders.
- Use Lasso feature selection method to explore the importance of each feature.
- Add noises into features to check the features are not randomly selected.
- Achievement:
- Certificate of Honorable Mention.
Education
National Chenchi University, MS, Statistics, 2015 – 2017
- GPA : 3.84 / 4.0
- Master Thesis: Entropy Based Feature Selection, Professor Pei-Ting, Chou
- Objective: Build a similarity matrix based on Mutual Entropy under Hierarchical Clustering. Afterwards, select clustered features as the final selection.
- Compare the model with other feature selection methods like RF, Lasso, F-score.
National Chen-Kung University, BS, Mathematics, 2011 – 2015
Skills
Programing
- Python
- Scala
- R
- MSSQL
Data-related Tools
- Tensorflow (Keras)
- PyTorch
- Spark
- Docker
- Scikit-Learn
- Pandas
Cloud Platform
- AWS
- GCP
Language
- English: TOEFL 98 / 120
Work Experience
CTBC Bank, Model Development Department, Data Scientist
2021.12 – present
- About the department:
- Responsible for developing models related to bank recommendations and risks, including projects such as coupon recommendations, account opening marketing lists, and fraud detection.
- Job responsibilities:
- Throughout the entire project lifecycle, my primary responsibilities included model design, model training, end-to-end process development, feature design, performance tracking, and method research.
Fraud Alert Project
- Objective:
- Predicting potential fraudulent accounts based on transaction data, restricting transactions in advance to prevent harm.
- Responsibilities/Achievements:
- Development and deployment of credit card and financial features.
- Managing the data flow process from receiving variables to model predictions, identifying risk factors, and updating alert lists.
- Implemented Autoencoder + contrastive learning to achieve a 1.81% improvement in model effectiveness.
Coupon Recommendation
- Objective:
- Personalized coupon recommendations for mobile banking users to increase click-through rates and redemption rates.
- Responsibilities/Achievements:
- Utilized multi-task learning to simultaneously predict click-through behavior and coupon redemptions, resulting in a 14% increase in click-through rate and a 74% increase in redemption rate.
- Created performance tracking reports to monitor online model performance and provide insights to Business Units.
Financial Product Recommendations
- Objective:
- Tailored financial product recommendations for mobile banking users to enhance click-through rates without compromising conversion rates.
- Responsibilities/Achievements:
- Applied multi-task learning to jointly learn click-through and conversion behaviors, fine-tuned model architecture, achieving a 90% outperformance against competitor models in online testing.
Marketing List for Digital Savings Accounts
- Objective:
- Optimized conversion rates for marketing lists related to digital savings accounts
- Responsibilities/Achievements:
- successfully raising conversion rates from 0.23% to 1.16%
Work Experience
CLICKFORCE, Data Engineer Supervisor, 2020.1 – 2021.11
- About the company:
- As a top domestic digital advertisement company, CLICKFORCE cooperates with over 900 web media and over 400 mobile media to build a huge advertising environment. CLICKFORCE considers data-driven solution as the core concept of the company, and dedicates to help advertisers to achieve their commercial goals.
- At 2020, CLICKFORCE won 2 awards at Agency & Advertiser of the Year.
- Successfully acquire the exclusive advertising agency qualification for Tokyo 2020 Olympics in Taiwan.
- Job responsibilities:
- Optimize ad performance from all aspects, including the system, target audience tags, etc.
- Do researches for new ML model (recommender model, NLP model) or architecture which is suitable for our system.
- Develop data-related products or projects.
- Analyze data to help improve our system or inspect whether the demands from business side is doable.
Real-time AD Recommender System
- Objective:
- Building a real-time ad recommender system to upgrade our ad server and get better performance.
- Responsibilities:
- Figure out what kind of recommender system components that is suitable for our ad system.
- Build a tower-like and feature-cross model refer to other famous recommender system model.
- Responsible for system engineering, which includes data preprocessing, embedding generates, memory cache, cold start, model API, etc.
Interest Tags
- Objective:
- Build interest tags for ads to help ad optimizers choose their target audience.
- Responsibilities:
- Create the features from what articles they saw, what website they viewed, and what ads they interacted.
- Deal with 20 million rows data and 120 million inference samples.
- Build ML model to predict each user's behavior on certain ads.
- Using Spark through AWS EMR to accelerate the speed of producing tags.
- Achievements:
- Raise CTR performance up to 200-300% of the original tags depends on different tags, and gain more impression while maintain better performance.
- After accomplishing this project, we terminated the cost on purchasing interest tags from other company, and successfully turned the original cost into revenue by providing profitable data.
First Party Cookie Mapping
- Objective:
- Deal with the Google 3rd party Cookie issue, figure out a method to map numerous 1st party Cookies to a user.
- Responsibility:
- Transform this problem into a ML mission. Design the label of the data, figure out what feature we can get or produce and whether the feature is useful for the goal.
- Apply XGboost on this mission.
- Build a small test to prove this method works.
- Achievement:
- 70% of precision.
- One of the solution of our company while the cancelation of 3rd party Cookie happen.
Invoice Data Application
- Objective:
- Develop invoice data application.
- Responsibility:
- Responsible for fine-tuning BERT to predict category for each product.
- Produce invoice data report to brands or business unit. It demonstrates the sales volume across different channel, what kind of products are frequently bought together, and also shows comparison of target brand to the other brands.
- Achievements:
- Produce an invoice data report product.
- Produce invoice tags for ad system.
Other Experience
E.Sun AI 2020 Summer Competition, 2020.7 – 2020.8
- Objective:
- Extract names of money laundering suspects from an article.
- Responsibilities:
- Crawl the articles from different media, and parse them by using Selenium, Requests, and Beautiful Soup.
- Construct 2-step model: First, identify whether the article is related to money laundering. Second, extract the suspects' names.
- Build model serving API by Tensorflow Serving.
- Build REST API for preprocessing request data and return the prediction.
- Achievement:
- 23rd place among 409 teams.
Youtube Data-Driven Marketing System, Institute for Information Industry, 2019.8 – 2019.11
- Objectives:
- Use the title and the description of videos to automatically classify videos.
- Use the title and the description of videos to identify whether a video is sponsored.
- Give suggestions for Youtubers or companies who desire to sponsor in a video based on data analysis.
- Responsibilities:
- Apply Google API and write Python functions to get structured raw data.
- Train word vectors using Gensim based on Wiki's open data.
- Use the frequency of each sentence as a criteria to eliminate useless words.
- Tune LSTM, Conv1D, BERT on the NLP mission.
- Use EDA methods to see the insights of the data under different classes and different sponsored status.
- Achievement:
- 71% accuracy in classifying video’s type.
- 89% accuracy in detecting sponsored content.
E.Sun Real Estate Price Prediction Competition, 2019.7 – 2019.8
- Objective:
- Use the real estate training data to build a model and predict the real estate price within 10% residual.
- Responsibilities:
- Apply XGBoost, LGBM and other ML models to train the model.
- Collect the outputs as new features from each ML model and add them into the original data set to enhance the performance of the final model.
- Achievement:
- 150th place out of 1200 teams.
KKTV Data Game，2017.5 – 2017.6
- Objective:
- Predict the next video a user watch in the next time interval.
- Responsibilities:
- Extract different features from raw data, such as the latest video, the video which got the longest viewing time, the video which got the largest number of viewing.
- Use the user viewing data to construct a similarity matrix of each video as additional features.
- Achievement:
- 10th place out of 50 teams.
MRT Open Data Competition, 2017.4 – 2017.5
- Objective:
- Study the changes of passenger volume of MRT by surrounding geometric data.
- Responsibilities:
- Apply bisection method to build the edges between MRT stations.
- Combine other geometric data based on these borders.
- Use Lasso feature selection method to explore the importance of each feature.
- Add noises into features to check the features are not randomly selected.
- Achievement:
- Certificate of Honorable Mention.