轉職熱搜工作

1.數據治理實施，包括資料模型、資料定義、元數據、資料品質等 2.建構數據工作流(Data Pipeline)、導入數據工程自動化和敏捷實踐(DataOps) 3.熟悉數據架構規劃、數據源開發、數據蒐集與處理、數據探勘與加值 4.數據建模 : 依主題挑選適當模型方法與參數處理進行多類型建模與冠軍模型挑選，與商業分析師合作進行統計分析模型及最佳化的研究與開發。 5.視覺化應用 : 善用視覺化工具(PowerBI)溝通特徵值與模型分析結果。 6.具備跨單位的大型專案管理實務 7.具備良善的溝通技巧及專案團隊協作能力

Job Summary: The Machine Learning engineer will be responsible for computer vision related projects, particularly in advanced image/video processing analyses and utilized for improving product quality, production yield, and factory effectiveness, along with other R&D activities such as algorithm designs, prototype developments and experimental designs. Key Responsibilities: • Design, implement, and refine advanced Machine Learning/Deep 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. • Apply deep learning and computer vision techniques, such as image classification, object detection, and 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 modeling techniques to improve decision-making to customers and stakeholders. • Collaborate effectively with team members, whether leading projects or supporting initiatives led by others. • Research and evaluate new data technology and state-of-art algorithms, and keep up to date with market trends and competitive insights.

Job Summary/Role: The Big data developer team works closely with the manufacturing functions in all relative data fields, such as, 1) connecting and automating the data pipeline from MFG file servers, API, RDB, etc., 2) building structurally data warehouse and data market for flexible application or dashboard, and 3) also handling data cleansing and data governance. In order to accelerate the development of application/analysis, Big data developer will be the individual contributor with strong experience in the technologies of Big Data, Data warehouse, SQL and NoSQL etc. Also Big data developer will also work closely with data analysts/data scientists to solve the real MFG cases. Responsibility: • Collaborate with users, data analysts, data scientists to develop flowcharts, layouts and documents to identify solutions. • Architect on real MFG needs and translate requirements into technical needs. • Identify and understand the data sources that are relevant for analysis, and implement efficient data engineering functions (including data ingestion, cleaning, ETL, integration, etc.) • Consolidate the requirements and using data engineering skills to design a robust Data Mart. • Participate in design, architecture review and deployment of Big data and data warehouse solutions. • Design for automation and monitoring mechanism in a continuous integration environment. • Bring new data technology with POC to solve existing data challenges. • With high performance and comprehensive SQL to process data in relational databases (or Map/Reduce style querying for NoSQL databases). • Work closely with user/IT/data science team to design and develop scalable data pipeline or end-to-end analytical workflow. • Provide direction, training, and guidance to less experienced team members.

1.現場作業空間、動線、流程、動作之規劃與SOP撰寫。 2.物流系統資料分析、協助業務專案推動。 3.對倉儲相關設備有概念並對新科技應用有熱情。 4.軟體自動化應用(VBA)。 5.規劃分析空間、動線、流程。 6.對改善相關專案進行可行性分析。 7.改善現場發現問題。 8.協助各現場作業改善提升效率。 9.推行軟、硬體自動化作業。 10.協助主管專案推行及主管交辦事項。

【職位說明】 參與智慧城市 AI 賦能產品開發，負責建置企業級數據平台(Data as a Service, DaaS)，處理各種形式的資料，並進行完善的資料治理、血緣追蹤與監控。最終將整合生成式 AI（Generative AI）功能，提供智慧城市解決方案所需的各種應用服務。 【工作內容】 • 建置資料處理流程，涵蓋結構化與非結構化之資料 • 建置資料數據服務 API，並設計資料視覺化分析 • 整合生成式 AI 應用（如 LLM、RAG）至資料流中 • 負責資料治理、血緣追蹤、系統監控作業，以及效能調優與異常排查

【關於PropTech數位人才招募計劃】 信義企業集團的各項產品與計畫正融入大數據、機器學習與人工智慧等應用，我們的數位團隊具有豐沛的研發能量，截至目前為止，已累積有40多件發明專利與20件設計專利，大幅領先於房仲相關產業者；並持續致力於「房地產科技創新」與「數位居住生活」兩大面向，以創新智能工具，為使用者提供超乎想像的服務體驗。 我們希望讓居住在這片土地上的人們，在「不動產交易買賣」與「居住生活日常」有更好的保障與體驗，也期待你與我們一起，加速這個改變。 這裡匯聚了40年的集團產業資源，以及多元化的人才；接下來，想邀請你加入，與我們一起創造超乎想像的體驗，成就即將到來的未來！ 【職務簡介】 1.規劃與建置企業級數據平台架構（Azure Data Platform：Databricks、Data Factory、Fabric、Power BI），確保系統穩定性、擴展性與長期可持續發展。 2.設計並優化 AI 與 Data Services（Function App、SQL MI、API、Batch 等），支援 AI 模型部署、應用服務化與業務落地。 3.規劃跨雲數據與 AI 服務整合（Azure、GCP/Vertex AI、BigQuery ML），確保跨平台數據流通與應用效能。 4.建立並推動公司數據與 AI 架構標準，包含數據治理、安全合規、效能優化與成本控管。 5.設計高效率的 ETL / ELT 資料處理流程，確保數據整合的正確性、即時性與最佳化。 【基本條件】 1.3年以上數據工程、系統架構或大型數據平台建置經驗。 2.熟悉雲端平台（Azure 為主，具 GCP / AWS 經驗佳），具企業級數據平台規劃與維運能力。 3.精通資料庫設計與管理，包含關聯式、非關聯式及雲端資料庫。 4.熟悉 Python 或其他主要程式語言，能設計可擴展的數據處理流程。 5.具跨系統數據整合與高效能 ETL/ELT 工作流設計經驗。

WITS 專注於資訊軟體服務與人才管理業務發展，30多年來與上千家不同產業的企業合作，客戶橫跨多元產業，如：金融業、科技業、電訊業、製造業......等。加入我們，您將有機會與專業諮詢團隊合作，了解各不同行業與關鍵企業客戶的發展趨勢，並學習最先進的資訊軟體與人才管理知識。 工作內容: 1. 支持業務單位營運管理（50％）：負責管理和執行各項帳務、合約、庶務作業，確保準確及時處理。 2. 執行半導體產業、客戶分析（40％） : 分析各項商務、客戶數據數據，並協助中、高階主管商務決策建議。 3. 協助業務端人員關懷（10％）：協助維護團隊的正向工作氛圍，提供人員關懷與支持。

＜About the job＞ 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). ＜Job Description＞ *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).

您將成為臨床研究團隊中的關鍵角色，負責協助研究計畫建置、管理及維護臨床數據採集系統（EDC），確保數據完整性與研究合規性。 核心工作內容： • 協助建置、系統更新與維護臨床數據採集系統 • 執行臨床數據管理流程：資料匯入/匯出、查詢、清理、追蹤缺漏值 • 針對研究團隊與臨床單位進行系統操作訓練與技術支援 • 整合報表自動化需求（如 R 或 Python 協助數據摘要分析） • 協助與資訊部門對接系統安全與資料備援相關事宜 我們希望你具備： 學歷／背景： • 生物醫學、資訊管理、公共衛生、統計、護理、藥學等相關背景佳 • 有臨床試驗、研究資料管理、CRF/REDCap/EDC 經驗尤佳 技術技能： • 熟悉EDC 系統者優先 • 熟悉 SQL 查詢、Excel 進階功能、或具資料視覺化經驗尤佳 • 具備 Python / R / SAS （可支援數據前處理與簡易分析） • 熟悉資料隱私與 GCP / ICH E6 (R2) 為佳