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
Lead Engineer of CVD and Heat Treatment Process @PT Sumco Indonesia2018 ~ 現在
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Design Engineering
一個月內
ANDIKA NUR BIANTONO Design Engineering Continuous Improvement Engineering Bekasi, West Java, Indonesia I have 10 years of experience in a technical role as an engineer. I use some useful software (M.Office, Spotfire, JMP statistical software, CATIA, NX, Rhinoceros, AutoCAD) to do my work. My experience in designing automotive and shoe manufacturing (blow molding and Injection processes) as well as handling continuous improvement processes (mechanical, chemical and gas) for semiconductors and holds a Lean six sigma yellow belt. Work Experience Lead Engineer of CVD and Heat Treatment Process • PT Sumco Indonesia MarchPresent 1. Create documents
就職中
・
目前會考慮了解新的機會
全職 / 對遠端工作有興趣
10 到 15 年
Institut Teknologi Sepuluh Nopember Surabaya
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Shipbuilding
Lean Officer (with McKinsey & Company ) @ECOBANK PLC (Formerly Oceanic Bank Intl PLC) 2008 ~ 2009
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Lean and Six Sigma Practitioner
超過一年
abiodun adetula I help individuals and businesses to think and work smart, improve their processes & performance and create positive experiences with their Customers. I speak, teach, and facilitate brain storming sessions at conferences and retreats on Lean & Six Sigma, Customer Service, Supply Chain Management, Project Management & Soft Skills Lean and Six Sigma Practitioner City, NG [email protected] Work Experience ACCELTAGE CONSULTING, Chief Luminary Officer, Sep 2013 ~ Present ◼ Consultant for AIRTEL Nigeria and Ghana on Lean Six Sigma : In Ghana I Trained and Equipped the Cross Functional Teams CFTs made up of 45 employees with Lean
就職中
全職 / 對遠端工作有興趣
6 到 10 年
American Society for Quality
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Certified Six Sigma Black Belt, Certified Quality Process Analyst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
產業
工作年資
1 到 2 年
管理經歷
技能
語言能力
English
・
進階
求職偏好
希望獲得的職位
數據分析師、資料科學家
預期工作模式
全職
期望的工作地點
台灣台北
遠端工作意願
對遠端工作有興趣
接案服務
學歷
學校
台灣大學
主修科系
統計
Education
National Taiwan University Taipei, Taiwan
Statistics, Master Degree GPA 3.94/4.3 (Rank 1th) 2016.09 - 2018.06
National Chengchi University Taipei, Taiwan
Statistics, Bachelor Degree 2013.09 - 2016.06
Work experience
Vanguard International Semiconductor Co. Hsinchu, Taiwan
algorithm engineer 2018.10 - 2020.3- Designed an anomaly detection model for monitoring the health of semiconductor manufacturing equipments.
- Built and combined three models, Moving Average Model, AutoEncoder and Multi-Scale Convolutional Recurrent Encoder Decoder (MSCRED), to improve higher accuracy.
- Used SAS JMP to perform data preprocessing and python with Tensorflow framework to build the model.
- Saved every module engineer 1 hour per day.
- Designed a wafer defect detection and classification model on photos provided from Automated Optical Inspection (AOI).
- Used object detection model, Faster R-CNN, with Tensorflow framework.
- Achieved 85% accuracy, and 90% recall rate.
- Design Automatic Virtual Metrology to update the parameter settings of equipments in real time.
- Used Dense Neural Network with Keras framework.
- Equipment parameters were estimated and updated
Publication
Semiparametric regression analysis of current status data under sequential monitoring
- Developed a semiparametric estimation method for regression analysis based on the sequential monitoring data.
- Introduced the additive hazards regression on sequential data to utilize the comprehensive monitoring information.
- Proposed a two-stage estimation procedure by pooling the sequence of the current status at monitoring times to estimate the regression coefficients in the semiparametric additive hazard model.
- Used R to conduct extensive simulation studies with various censoring rates and monitoring frequencies to investigate the performance. The result indicated that this model has a good performance, which has bias less than 0.01 and is close to the right censored data result.
Award
The 5th E.SUN commercial bank SAS competition: excellent work
Big Data Data Scientist Competition Text Analysis and Digital Marketing Competition
- Lead a team of four, including one member majoring marketing, to develop feasible marketing strategy candidates, and then designed further analyzing procedures and models respectively.
- Implemented descriptive statistics with SAS Text Miner to analyze forum texts and search logs from the official site of E.SUN, to identify different costumer groups and their corresponding consumption propensities.
- Implemented a Decision Tree model, with SAS, SAS VA and SAS Viya, to predict customers’ purchasing power based on customer profile and their credit card history, which achieved 86% accuracy.
- Based the analysis and model, we selected the most important variables and decided our major target group, and proposed our final marketing strategy.
Skills
- python, R,
- SAS, SAS JMP
- pytorch, tensorflow, keras
selected courses
- Mathematical statistics (2016 Fall) A
- Applied Bayesian statistical method (2016 Fall) A-
- Biostatistics research methods (2016 Fall) A
- Principles and applications of computational biology (2017 Spring) A
- Machine learning (2017 Spring) A-
- Advanced Medical Statistics Method 1 (2017 Fall) A+
- Survival analysis (2018 Spring) A+
- Category analysis (2018 Spring) A+
Education
National Taiwan University Taipei, Taiwan
Statistics, Master Degree GPA 3.94/4.3 (Rank 1th) 2016.09 - 2018.06
National Chengchi University Taipei, Taiwan
Statistics, Bachelor Degree 2013.09 - 2016.06
Work experience
Vanguard International Semiconductor Co. Hsinchu, Taiwan
algorithm engineer 2018.10 - 2020.3- Designed an anomaly detection model for monitoring the health of semiconductor manufacturing equipments.
- Built and combined three models, Moving Average Model, AutoEncoder and Multi-Scale Convolutional Recurrent Encoder Decoder (MSCRED), to improve higher accuracy.
- Used SAS JMP to perform data preprocessing and python with Tensorflow framework to build the model.
- Saved every module engineer 1 hour per day.
- Designed a wafer defect detection and classification model on photos provided from Automated Optical Inspection (AOI).
- Used object detection model, Faster R-CNN, with Tensorflow framework.
- Achieved 85% accuracy, and 90% recall rate.
- Design Automatic Virtual Metrology to update the parameter settings of equipments in real time.
- Used Dense Neural Network with Keras framework.
- Equipment parameters were estimated and updated
Publication
Semiparametric regression analysis of current status data under sequential monitoring
- Developed a semiparametric estimation method for regression analysis based on the sequential monitoring data.
- Introduced the additive hazards regression on sequential data to utilize the comprehensive monitoring information.
- Proposed a two-stage estimation procedure by pooling the sequence of the current status at monitoring times to estimate the regression coefficients in the semiparametric additive hazard model.
- Used R to conduct extensive simulation studies with various censoring rates and monitoring frequencies to investigate the performance. The result indicated that this model has a good performance, which has bias less than 0.01 and is close to the right censored data result.
Award
The 5th E.SUN commercial bank SAS competition: excellent work
Big Data Data Scientist Competition Text Analysis and Digital Marketing Competition
- Lead a team of four, including one member majoring marketing, to develop feasible marketing strategy candidates, and then designed further analyzing procedures and models respectively.
- Implemented descriptive statistics with SAS Text Miner to analyze forum texts and search logs from the official site of E.SUN, to identify different costumer groups and their corresponding consumption propensities.
- Implemented a Decision Tree model, with SAS, SAS VA and SAS Viya, to predict customers’ purchasing power based on customer profile and their credit card history, which achieved 86% accuracy.
- Based the analysis and model, we selected the most important variables and decided our major target group, and proposed our final marketing strategy.
Skills
- python, R,
- SAS, SAS JMP
- pytorch, tensorflow, keras
selected courses
- Mathematical statistics (2016 Fall) A
- Applied Bayesian statistical method (2016 Fall) A-
- Biostatistics research methods (2016 Fall) A
- Principles and applications of computational biology (2017 Spring) A
- Machine learning (2017 Spring) A-
- Advanced Medical Statistics Method 1 (2017 Fall) A+
- Survival analysis (2018 Spring) A+
- Category analysis (2018 Spring) A+