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De 4 a 6 años
6-10 años
10-15 años
Más de 15 años
資深資料工程師 @緯創資通股份有限公司2020 ~ Presente
・
Data Analyst、Data Engineer、Data Scientist、Customer Experience Analyst、Solution Architect、Cloud Architect
En un mes
Empleado
・
Listo para la entrevista
A tiempo completo / Interesado en trabajar a distancia
De 4 a 6 años
元智大學 Yuan Ze University
・
工業工程與管理學所
Product Design Consultant @Freelancer2023 ~ 2024
・
UX Researcher / UIUX Designer / Product Designer
En un mes
Ching You Hi, I'm Ching You, a Product Designer with six years of experience developing digital products. I specialize in creating an indulging atmosphere for digital products and creating logical user flow based on the result of UX research. As I embark on the next chapter of my career, I eagerly seek opportunities to contribute my wealth of experience and pioneering spirit to a prominent digital product company on the global stage. Let's explore new horizons together! Taipei City, Taiwan https://chingyou.webflow.io 工作經歷 Product Designer • 時
Empleado
・
Listo para la entrevista
A tiempo completo / Interesado en trabajar a distancia
De 4 a 6 años
Shih Chien University
・
B.F.A Communication Design
前端工程師 @Lctech_雷麒科技有限公司2021 ~ Presente
・
Front-end Engineer
En un mes
彭靖鈞 四年前端經驗，熱愛思考與解決問題所帶來的成就感，希望能找到志同道合的工作夥伴。 Taoyuan City, Taiwan• [email protected] 工作經歷 前端工程師 • Lctech_雷麒科技有限公司 七月Present 開發 JKF 旗下的 JVID 前端系統 與團隊協同將舊有的 PHP 專案重構為前後端分離 編
Empleado
・
Listo para la entrevista
A tiempo completo / Interesado en trabajar a distancia
De 4 a 6 años
南台科技大學
・
資訊工程系Desempleado
・
Listo para la entrevista
A tiempo completo / Interesado en trabajar a distancia
De 4 a 6 años
iSpan資展國際
・
前端工程師就業養成班
Past
Account manager @iSPOT Media 艾斯博媒體股份有限公司2023 ~ 2024
・
資深數位行銷專員、資深電商行銷專員
En un mes
我是蔡楓淋 總共有四年數位行銷及電子商務 0 ～ 1 經驗，策劃營運與媒體佈局，擬定年度行銷檔期及 D2C 規劃，投放數位媒體廣告（ Meta Ads、Google Ads、LINE LAP )並單一媒體百萬投放經驗，搭建完整漏斗佈局，注重數據成效分析與運用 CRM 分眾溝通，過往經營品牌電
Desempleado
・
Listo para la entrevista
A tiempo completo / Interesado en trabajar a distancia
De 4 a 6 años
中華醫事科技大學
・
資訊管理
Past
助理工程師 @第一社會企業股份有限公司2023 ~ 2023
・
MIS程式設計師,軟體設計工程師,Web開發工程師
En un mes
— 王心妤 Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat. Ut wisi enim ad minim veniam, quis nostrud exerci tation ullamcorper suscipit lobortis nisl ut aliquip. New Taipei City, Taiwan 工作經歷 三月Present 資訊工程師 車麗屋汽車百貨股份有限公司 1.使用.Net core 6 MVC開發LINE會員專區 (1)前端使用DEVEXPRESS+html+JavaScript (2)後
Desempleado
・
Listo para la entrevista
A tiempo completo / Interesado en trabajar a distancia
De 4 a 6 años
萬能科技大學
・
資訊管理
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
En un mes
陳勤霖 神經工程博士背景的數據分析師 Ph.D. in Neuroscience from Neuroengineering lab I have 5 years of hands-on experience in image and data analysis with biotechnology innovation projects. Dependable ability in managing collaborative projects to success. Business-driven motivation to apply analytic skills to optimize the product and its development procedure. https://chinlinchen1312.wixsite.com/chin-lin-chen 工作經歷 一月十二月 2023 博士後研究員 洛桑大
Desempleado
・
Listo para la entrevista
A tiempo completo / Interesado en trabajar a distancia
De 4 a 6 años
洛桑聯邦理工學院(EPFL)
・
神經科學
.NET開發工程師 @趣遊科技有限公司2023 ~ Presente
・
全端/後端工程師
En un mes
紀孟佐 曾經是電腦硬體工程師| 網頁後端工程師 | 國立台北科技大學碩士畢業 個性開朗、善於溝通，與各單位合作，目前是全端工程師。 [email protected] 技能 Front-end HTML/CSS JavaScript Back-end C/C++ JAVA C# ASP.MVC/APS.NET.CORE MSQL Elasticsearch Redis Other Git/GitLab/GitHub Docker CI/CD Jenkins
Empleado
・
Listo para la entrevista
A tiempo completo / Interesado en trabajar a distancia
De 4 a 6 años
國立台北科技大學
・
電機工程系(計算機組)Past
資深前端工程師 Senior Front-End Developer @法樂設計有限公司2019 ~ Presente
・
資深前端工程師
En un mes
Desempleado
・
Listo para la entrevista
A tiempo completo / Interesado en trabajar a distancia
De 4 a 6 años
National Yang Ming University
・
生醫光電所
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
En un mes
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
Desempleado
・
Listo para la entrevista
A tiempo completo / Interesado en trabajar a distancia
De 4 a 6 años
國立臺北科技大學
・
資訊工程El plan de reclutamiento más ligero y eficaz
Busque currículums y tome la iniciativa de ponerse en contacto con los solicitantes de empleo para lograr una mayor eficacia en la contratación. La elección de cientos de empresas.
- Examinar todos los resultados de la búsqueda
- Acceso ilimitado para iniciar nuevas conversaciones
- currículos accesibles sólo para empresas de pago
- Ver dirección de correo electrónico y números de teléfono de los usuarios
Consejos de búsqueda
1
Search a precise keyword combination
senior backend php
2
Use quotes to search for an exact phrase
"business development"
3
Use the minus sign to eliminate results containing certain words
UI designer -UX
Sólo los currículums públicos están disponibles con el plan gratuito.
Actualiza a un plan avanzado para ver todos los resultados de la búsqueda incluyendo decenas de miles de currículums exclusivos en CakeResume.
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
Situación actual
Empleado
Progreso en la búsqueda de empleo
Abierto a oportunidades
Professions
Data Scientist, Machine Learning Engineer
Fields of Employment
Banca, Inteligencia Artificial / Aprendizaje Automático, AdTech / MarTech
Experiencia laboral
De 4 a 6 años
Management
Ninguno
Habilidades
Idiomas
English
・
Fluido
Job search preferences
Posición
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
Tipo de trabajo
A tiempo completo
Ubicación
台灣台北, 台灣新北市
A distancia
Interesado en trabajar a distancia
Freelance
Sí, soy un autónomo amateur.
Educación
Escuela
政治大學
Mayor
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.