科熱臺灣有限公司

負責大型專案的系統開發、測試和維護支援。 內容包含： 1. 系統分析、設計和開發 2. 擬定測試計畫、測試案例/腳本、建置測試環境、準備測試資料和執行測試 必要條件 – 熟悉前端 HTML/CSS/JavaScript 等技術。 – 熟悉後端 Java 程式開發。 – 熟悉關聯式資料庫設計。 – 了解 Git 開發流程。 – 邏輯分析能力強，可獨立運作，擁有解決問題的能力。 – 能有效應付緊湊的專案進度。

負責帶領團隊進行大型專案的系統開發、測試和維護支援。 內容包含： 1. 系統分析、設計和開發 2. 擬定測試計畫、測試案例/腳本、建置測試環境、準備測試資料和執行測試 3. UAT和系統上線支援 必要條件 – 熟悉前端 HTML/CSS/JavaScript 等技術，並有前端框應用架開發經驗。 – 熟悉後端 Java 程式開發，並有後端框架應用開發經驗 – 熟悉關聯式資料庫設計。 – 熟練 Git 工作流程。 – 可獨立快速研究學習新技術。 – 邏輯分析能力強，可獨立運作，擁有優異解決問題的能力。 – 有帶領專案小組開發實務經驗，能有效應付緊湊的專案進度。 – 可以英文進行基本文件撰寫。

負責大型專案的系統開發、測試和維護支援。 內容包含： 1. 系統分析、設計、開發和設計 2. 擬定測試計畫、測試案例/腳本、建置測試環境、準備測試資料和執行測試 必要條件 – 熟悉前端 HTML/CSS/JavaScript 等技術，最好有一種前端框應用架開發經驗。 – 熟悉後端 Java 程式開發，並有後端框架應用開發經驗 – 熟悉關聯式資料庫設計。 – 熟練 Git 工作流程。 – 可獨立快速研究學習新技術。 – 邏輯分析能力強，可獨立運作，擁有優異解決問題的能力。

– Develop e-commerce mobile application using React Native under an agile environment – Collaborate with project manager, UI designer, business analyst, system analyst and analyst programmer to implement and develop mobile apps – Conduct design / code review to ensure the system quality – Provide support during assurance test, user acceptance test and backend system integration/testing

Job Responsibilities - Plays a key role and support customer in adapting and validating group-level data science solutions for successful implementation across local markets. - Ensure technical fit, conduct model evaluation, and support deployment-readiness. Job Requirements - Bachelor's degree in business, Information Technology or relevant disciplines - At least 4 years of working experience in Data Scientist - Ability to conduct in-depth exploratory data analysis (EDA) to identify market-specific data patterns and business nuances - Experience in adapting and fine-tuning predictive or recommendation models (e.g., LightGBM, collaborative filtering) for localized performance - Skilled in evaluating model effectiveness using both offline metrics and post-deployment KPIs; capable of detecting drift and misalignment - Proficient in Python and SQL, with hands-on experience using Spark (PySpark or Scala) for distributed data processing - Demonstrated ability to wrangle and transform varied datasets, engineer features, and tune models to improve predictive accuracy - Familiar with translating business objectives into technical model criteria, and interpreting model results in commercial terms - Comfortable collaborating with ML Engineers to ensure scalable and production-grade deployments - Prior exposure to retail, customer analytics, or marketing-related data science is preferred"

Job Responsibilities - Support customer in adapting and deploying group-level ML solutions across business units on cloud platform. - Enhance R&D code into production-grade pipelines, setting up APIs, ensuring CI/CD compliance - Collaborate with infra teams to ensure operational readiness.   Job Requirements - Bachelor’s degree in business, Information Technology or relevant disciplines - At least 4 years of working experience in Data Machine Learning - Hands-on experience deploying machine learning models in cloud environments, including containerization (e.g., Docker) and orchestration frameworks - Proficient in building and adapting ML pipelines using existing codebases or templates, with the ability to customize configurations for BU-specific needs - Solid understanding of CI/CD practices and MLOps workflows (e.g., GitHub, Cloud Build, model registries) - Experience designing and maintaining end-to-end ML pipelines, covering data ingestion, preprocessing, model serving, and logging - Skilled in API deployment using frameworks such as Flask or FastAPI; capable of containerizing and scaling model services - Strong proficiency in writing modular, testable, and production-grade Python code following software engineering best practices - Familiarity with relational and NoSQL databases; understanding of vector databases (e.g., FAISS, Pinecone, Chroma) is a strong advantage - Strong troubleshooting and debugging skills across data, code, infrastructure, and runtime layers - Experience with distributed data processing tools (e.g., Spark) and cloud-native services - Collaborative and results-driven, with the ability to work effectively across functions with Data Scientists, QA Engineers, and Product Managers" Translate above post into Traditional Chinese for me (as clear and simple/straight as possible)