CakeResume 找人才
進階搜尋
On
4 到 6 年
6 到 10 年
10 到 15 年
15 年以上
曾任
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
一個月內
轉為簡體，最終使用情感分析工具(snownlp)進行股市漲跌分析。 技術：NLP、Beautifulsoup、 opencc 、 snownlp 、 Matplotlib 台灣景氣指標預測模型 side project 使用深度學習框架(Pytorch)自行搭建預測模型，以各式台灣景氣指標當作輸入，輸出未來經濟景氣趨勢階段。 技術：Pytorch、Pandas、Numpy、 Sklearn 論文發表 • Chen, X. Z., Li
待業中
・
正在積極求職中
全職 / 對遠端工作有興趣
4 到 6 年
國立臺北科技大學
・
資訊工程
Algorithm Research & Development @適着三維科技股份有限公司 TG3D Studio Inc.2021 ~ 現在
・
Software Engineer
一個月內
就職中
・
正在積極求職中
全職 / 對遠端工作有興趣
4 到 6 年
國立台灣大學
・
生物產業機電工程所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
一個月內
就職中
・
正在積極求職中
全職 / 對遠端工作有興趣
4 到 6 年
國立政治大學（National Chengchi University）
・
資訊科學系曾任
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
一個月內
待業中
・
正在積極求職中
全職 / 對遠端工作有興趣
4 到 6 年
洛桑聯邦理工學院(EPFL)
・
神經科學
智慧製造全端開發工程師 @聯華電子股份有限公司2022 ~ 現在
・
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、Ai Application Engineer,Machine Learning Engineer,Deep Learning Engineer,Data Scientist
一個月內
用機器及降低故障率。 生產設備數據分析，製程問題進行分析，檢視錯誤原因，如: 負擔過重、溫度過高等。 Skills Python Langchain Numpy OpenCV Tensorflow (tf.keras) Pytorch Scikit-learn C# EmguCV ASP.NET Windows Forms 程式設計開發、單元測試專案 Halcon 影像處理 影像定位 瑕疵判斷 物件量測 Skills Database MySQL Oracle ArangoDB LLM Tool:langchain Vector DB
就職中
・
正在積極求職中
全職 / 對遠端工作有興趣
4 到 6 年
元智大學 Yuan Ze University
・
工業工程與管理學系所
Data Engineer @TSMC 台積電2022 ~ 現在
・
資料分析師、演算法工程師、軟體工程師、軟體專案管理
一個月內
various database. establish a CI/CD pipeline to kubernetes for the inner platform to imporving the seculity and stable of the process. NLP Engineer,–, Ubestream. I Led and designed a chatbot service project in EC, using Django and MongoDB. To develop NLU services with Huggingface, PyTorch, and Keras, including intent classification, NER, etc. At the same time, led a junior engineer team to set and complete the tasks, team members including 18 interns and 4 full-time engineers. And I help to prepare the product demo encompassing internal sharing, investor fundraising, and international
就職中
・
正在積極求職中
全職 / 對遠端工作有興趣
4 到 6 年
國立中央大學 National Central University
・
網路學習科技研究所
AI Engineer @Playsee2022 ~ 現在
・
資料分析師、資料科學家、產品經理
一個月內
李昀庭 Data scientist Taiwan 技能 Machine learning and Engineering skills: Python, Big Query, Google Storage, Linux, Docker, GCP, AWS, Scikit-learn, Tensorflow, Pytorch, MLOps, FastAPI, Machine Learning, Deep Learning, Computer Vision, NLP Experimental design, Project management, Product design English - TOEIC 725 工作經歷 AI工程師 Playsee NovPresent Taipei, Taiwan 自動化標註推薦系統 設計並實踐架構取代25個標註者並及時標記和篩選視頻審核內容。 設計並優化影片
就職中
・
正在積極求職中
全職 / 對遠端工作有興趣
4 到 6 年
National Cheng Kung University
・
心理所（認知科學所）
曾任
Senior Firmware Engineer @Artesyn Embedded Technologies2019 ~ 2022
・
韌體工程師/軟體工程師/控制工程師/演算法工程師/
一個月內
Ted Li Senior Firmware Engineer Over 6 years of firmware/software development expertise as a Senior Firmware Engineer, specializing in embedded systems, cross- functional projects, and RL-optimizations. Driving global technical innovations and training. New Taipei City, Taiwan [email protected] https://github.com/armcortex https://www.linkedin.com/in/ted-li/ https://about.armcortex.cc/ Skill Programming C/C++ Python Bash SQL AI (PyTorch, TensorFlow, Keras) Tool RTOS Embedded System Git Docker/Docker Compose
待業中
・
正在積極求職中
全職 / 對遠端工作有興趣
6 到 10 年
日本電氣通信大學 The University of Electro-Communications (UEC)
・
Robotics Engineering
全端工程師 @中冠資訊股份有限公司2021 ~ 現在
・
網站後端工程師
一個月內
喬康豪 full-stack-engineer 前端技能 : vue.js, angular, sass, restful api, websocket 後端技能 : node.js, spring boot, , rabbitmq, websocket, k8s 工作經歷： full-stack-engineer: 3~4年 Fontend-engineer： 2~3年 Deep-learning engineer： 2~3年 Android app developer： 1年 email： [email protected] Skliis Font-End Vue.js angular Sass websocket RWD Back-End spring boot rabbitmq websocket Restful Api nodejs laravel Deep-Learning Object Detection Object Tracking YOLO pytorch 開發工具及環境 git docker k8s
就職中
・
正在積極求職中
全職 / 對遠端工作有興趣
6 到 10 年
清華大學 National Tsing Hua University
・
核子工程 nuclear engineering
曾任
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
一個月內
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
待業中
・
正在積極求職中
全職 / 對遠端工作有興趣
4 到 6 年
台灣大學
・
大氣科學所最輕量、快速的招募方案，數百家企業的選擇
搜尋履歷，主動聯繫求職者，提升招募效率。
- 瀏覽所有搜尋結果
- 每日可無限次數開啟陌生對話
- 搜尋僅開放付費企業檢視的履歷
- 檢視使用者信箱 & 電話
搜尋技巧
1
嘗試搜尋最精準的關鍵字組合
資深 後端 php laravel
2
將須完全符合的字詞放在雙引號中
"社群行銷"
3
在不想搜尋到的字詞前面加上減號，如果想濾掉中文字，需搭配雙引號使用 (-"人資")
UI designer -UX
職場能力評價定義
專業技能
該領域中具備哪些專業能力（例如熟悉 SEO 操作，且會使用相關工具）。
問題解決能力
能洞察、分析問題，並擬定方案有效解決問題。
變通能力
遇到突發事件能冷靜應對，並隨時調整專案、客戶、技術的相對優先序。
溝通能力
有效傳達個人想法，且願意傾聽他人意見並給予反饋。
時間管理能力
了解工作項目的優先順序，有效運用時間，準時完成工作內容。
團隊合作能力
具有向心力與團隊責任感，願意傾聽他人意見並主動溝通協調。
領導力
專注於團隊發展，有效引領團隊採取行動，達成共同目標。
專業背景
目前狀態
就職中
求職階段
專業
數據科學家, 機器學習工程師
產業
銀行, 人工智慧 / 機器學習, 廣告技術 / 行銷技術
工作年資
4 到 6 年
管理經歷
無
技能
語言能力
English
・
進階
求職偏好
希望獲得的職位
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
預期工作模式
全職
期望的工作地點
台灣台北, 台灣新北市
遠端工作意願
對遠端工作有興趣
接案服務
是，我利用業餘時間接案
學歷
學校
政治大學
主修科系
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.