1.Switching power supply design 2.Component FMEA 3.Functional test 4.Sample debug
1.伺服器電源/充電樁電源模組等電源產品設計 2.協助問题分析與追蹤、工程樣品製作與工程/設計變更評估 3.執行零件材料選用及供應商導入 4.使用DSP控制AC/DC拓樸, 並與EE協作 5.使用DSP控制DC/DC拓樸, 並與EE協作
1.瞭解產品制程及時程規劃，熟知設備開發導入週期； 2.非標自動化設備電路接線，信號測試，點位調試，設備參數設置，程序邏輯理解，安全防護及現場異常處理； 3.設備的改善、優化，維修、保養； 4.參與優化設備程序，提升設備產能。
1.AUTOSAR software component(SWC) 開發整合與驗證 2.熟悉MATLAB simulink MBD開發 3.具備車用零組件開發經驗(power/inverter/body control) 4.熟悉ASPICE流程尤佳
二. 工作內容: 1. 新產品硬體開發 (電路設計及layout佈局規劃) , 測試驗證 2. 與 Support Team Co-Work 解決問題 3. Bring-up, Debug, FA 4. 協助工廠端生產
1. 設計BLDC直流有刷/無刷馬達驅動電路； 2. 馬達驅動IC規格開發與驗證； 3. 風扇產品驗證及安規Debug解決方案； 4. 硬體系統功能測試驗證與問題分析； 5. 協助解決工程試產/生產製造； 6. 分析並解決產品/專案開發過程中硬體相關的技術問題；
1. Server/Storage 產品數位/類比訊號 電氣特性驗證計畫擬定與問題分析2. 跨部門訊號工程問題溝通3. 新高頻訊號測試技術研究與內部人員訓練
1.電商平台之需求分析 2.電商平台之開發與維運 3.電商平台之介接整合API開發 4.資料庫效能調校與問題排除 5.系統程式MVC架構規劃
1.產線SMT設備管理 2.生產流程規劃及執行 3.生產最佳化管理 4.工業4.0生產技術推動 5.TPS/VSM推動 6.客戶SPEC維護及導入EC/CR
1. 日常會計帳務審核及處理 2. 營業稅申報 3. 財務報表出具 4. 提供母公司財務報表 5. 每月付款資料準備 6. 會計憑證整理及歸檔 7. 跨部門以及與母公司相關部門溝通
As the Senior Data Engineer or Data Architect, you'll join the various advanced Data Science & AI Projects in the corporate headquarters. As well as developing intelligent applications via related AI and Big Data Analytics Technology for digital transformation, you will have plenty of opportunities to develop emerging applications based on different use cases and expand your tech skillset in this world-class company (Fortune Global 500, 20th). *Type 1：Senior Data Engineer Responsible for acquiring data using API, Web scraping, or other data accessing protocol/scripting and developing the ETL data pipelines, and the data aggregation systems. Using software development experience to design and build high-performance automated systems. As below: 1. Data Processing (1)Data ETL(Extracting/Transforming/Loading) process engineering and querying from relational data management(such as SQL Script). (2)Building systematic data quality processes and checks to ensure data quality and accuracy. (3)Solid coding experience in Python or Java. 2. Data Pipeline Development (1)Develops a data integration process, including creating scalable data pipelines and building out data services/data APIs. (2)Create a data processing automation and monitoring mechanism by optimizing the data pipeline process. (3)Experience with dataflow/workflow/management tools, such as Apache Nifi, Apache Airflow, Azkaban, etc., is preferred. 3. Data Crawling (1)Build scalable tools that automate web crawling, scraping, and data aggregation from various web pages using frameworks such as Scrapy. (2)Accessing data from REST APIs, particularly in parsing data in disparate formats such as JSON and XML, and developing automated engineering. (3) (Nice to Have) Knowledge of server-based front-end/UI technologies, including Vue/React and HTML/CSS, is preferred. *Type 2：Data Architect Responsible for designing, implementing, and maintaining scalable and reusable system architectures/data architectures for complex data structures and large data in data science and AI projects. As below: 1. Data Schema Design (1)Collaborate with the team to design DB/Table Schema and Data Schema. (2)Consolidate the requirements and use data engineering tech to design and implement a robust Data Mart. (3)Experience handling all kinds of structured/semi-structured/unstructured data and streaming data is preferred. (4)Hands-on experience with Dimensional Data Modeling(Column-based data warehouse) or NoSQL Schema design. 2. Data Platform Architecture (1)Design and build data infrastructure/platform components to support complex data pipelines ingesting various data from multiple internal and external data sources and processing. (2)Familiar with Big Data frameworks and processing technologies, ex: Hadoop, Apache Spark, NoSQL ...etc. (3)Familiar with AZURE or AWS cloud data services(hands-on experience with cloud infrastructure will be a plus) (4)Familiarity with the Linux OS environment, the Shell Scripting, and infrastructure knowledge. (5)(Nice to Have)Experience with declarative infrastructure/container technologies, such as Docker and Kubernetes (k8s/k3s). (Nice to Have).
1.為客戶提供及時有效的技術諮詢與技術支援 2.解決客戶在產品安裝及應用配置的問題 3.整合並驗證產品/服務/解決方案 4.收集客戶及相關行業訊息，回饋給業務及研發部門協助產品提升與創新
1.建立模擬測試 (MIL / SIL / HIL/ VIL) 測試環境，撰寫、維護、改善測試程式 2.Matlab/simulimk模型修改與優化 3.規劃模擬測試計畫與測試案例（test case）及測試腳本編寫 4.協助RD 韌體Issue改善及追蹤 5. EV法規研讀及測試環境建立 6.執行主管交辦事務 5. 供應商溝通、試產及量產導入
1.雲端產品/服務/解決方案之規劃 2.探索並分析客戶的需求，提出滿足客戶的解決方案，協助業務人員提高成交率、縮短成交時間 3.支援銷售業務與產品管理
1.彩盒及工業包裝設計 2.包裝材料打樣及確認 3.Label設計與樣品確認 4.系統工程文件維護 5.產線問題解決及異常處理
1.負責規劃與執行招募流程作業，包含需求確認、選才、面談與任用管理。 2.配合事業群發展執行相關招募策略與計畫。 3.以HRBP角度協助各部門人才發展。 4.執行人事行政作業、人資相關專案
Job Summary: The Statistician/Data Scientist will be responsible for all advanced statistical methods utilized for improving product quality, production yield, and factory effectiveness as well as finding the root-causes, along with other consulting services such as customer targeting and lead scoring in support of sales and marketing activities worldwide. Key Responsibilities: • Design, implement, and refine advanced statistical/Machine Learning models to support product quality improvement, increasing yield and factory effectiveness. • Cultivate strong relationships with production line engineers, IT, and other key stakeholders to ensure alignment of modeling initiatives with company objectives and to identify new hypotheses for model improvements. • Scale out modeling capacity by driving infrastructure improvements such as automation of data preparations, model training, implementation, and optimization. • Support integration of models into tools for use by product engineers and business analysts. • Engage with customers to develop and customized data analytics solutions to address special needs. • Address ad hoc queries from management and present actionable recommendations in a clear, concise, and convincing manner. • Communicate the application and benefits of using various predictive modeling techniques to improve decision-making to customers and stakeholders. • Collaborate effectively with team members, whether leading projects or supporting initiatives led by others. • Provide direction, training, and guidance to less experienced team members.
1.資料中心電力系統架構設計 2.與電力系統設備供應商及施工廠商協調合作
1.資料中心電力系統架構設計 2.與電力系統設備供應商及施工廠商協調合作
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