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【Hour Loop 飛輪電商】在2013年成立，並於2022年初在美國那斯達克上市。作為亞馬遜 (Amazon) 第三方賣家，我們的工作不只廠商開發，還包括商品上架、價格制定，以及物流與庫存管理，通通一手包辦。 在百萬競爭對手中，我們透過獨特的營運模式，成功實現十年來的持續成長，並脫穎而出，在2022年底躍升為亞馬遜 (Amazon) 前十名的賣家！ 我們提供彈性且暢通的職涯管道，不綁年資，端看績效表現，從「獨立貢獻者」到「管理職」適性發展！如果你喜歡打磨自身專業能力，鑽研獨立的工作項目，便適合獨立貢獻者的角色；若你有準確遠見，善於溝通和帶領團隊，你可能就是未來的帶人主管！ 一、採購策略與商品管理 (約 70%)1. 分析市場銷售數據與季節性，發掘潛力商品並擬定相應的採購策略。2. 協助業務經理制定採購計畫，以維持合理的庫存水位。3. 持續追蹤商品銷售表現，計算潛在利潤並提供最佳採購建議。4. 透過價格策略維持並優化商品毛利率。5. 建立與分析銷售報表，辨識衰退品項並挖掘新的成長機會。二、流程優化與專案管理 (約 30%)1. 與跨部門合作，透過數據分析與自動化設計具擴展性與高效率的流程解決方案。2. 運用數據分析工具（Excel、SQL、Python、R 等）找出問題根因並驗證改善方案。3. 能獨立規劃並執行專案，於時程內交付具質量與影響力的成果。4. 善用內部系統工具，提出可行的流程優化建議並推動落地。任職條件1. 具 1–2 年以上相關工作經驗尤佳。2. 熟悉 Excel 基礎公式與功能（如：sum, count, xlookup, pivot, chart, VBA），具良好的數字敏感度；若具備 SQL 語法能力（如：JOIN, 欄位值轉換, 套件應用）及視覺呈現軟體（如：Looker, Tableau, PowerBI）則更佳。3. 具備獨立專案執行能力，能準時完成並交付成果。4. 擅長跨部門協調整合，並具備良好的數據分析與洞察能力。

【 What You'll be Doing 】- Pipeline Implementation: Develop, maintain, and optimize robust ETL and real-time data pipelines to ensure high-quality data collection, processing, and storage.- System Monitoring: Actively monitor data pipeline health and troubleshoot data quality or performance issues to ensure high availability.- Database Management: Assist in designing data schemas and implementing database indexing or partitioning strategies for performance optimization.- API Tooling: Build and support backend APIs and internal data tools to empower SHOPLINE merchants with actionable insights through visualization platforms.- Collaboration: Work closely with Senior Engineers and Product Managers to translate business requirements into technical data solutions.

Established in 1987 and headquartered in Taiwan, TSMC pioneered the pure-play foundry business model with an exclusive focus on manufacturing its customers’ products. As of 2024, TSMC serves more than 500 customers and manufactures over 11,000 products for high-performance computing, smartphones, the Internet of Things (IoT), automotive, and digital consumer electronics. It is the world’s largest provider of logic ICs, with an annual capacity of 16 million 12-inch equivalent wafers. TSMC operates fabs in Taiwan as well as manufacturing subsidiaries in Washington State, Japan and China, and the Company began construction on a specialty technology fab in Dresden, Germany, in 2024. In Arizona, TSMC is building three fabs, with the first starting 4nm production in 2025, the second by 2028, and the third by the end of the decade. Are you a creative IT professional with strong technical aptitude who embraces changes and is passionate about data and information? We invite you to join our highly innovative data engineering team which is constantly designing, developing, and delivering high quality solutions for our customers. As a data engineer, you will have opportunities to work in a dynamic and fast-paced environment to collaborate with business functions to design solutions. You will translate business requirements into technical needs, connect and automate data pipelines, and deliver data architecture and data governance solutions. Responsibility: Perform IT system architecture design, new technology research, and provide recommendation.Design and implement optimal data pipeline architecture (considered high data volume, data governance, etc.).Work with PRODUCT/BIZ teams to assist with new data platform re-engineering or data-related technical issues.DataOps high availability NoSQL DB (e.g.: Cassandra, S3/MinIO, MariaDB, etc.) on K8s environment.

建構與優化機器學習模型: 運用多維度運動數據（如賽事數據、球員表現），開發球員薪資計算、表現預測等核心模型，並持續進行優化。 支援產品與行銷決策: 深入分析用戶行為與市場趨勢，提供數據洞察，協助產品團隊優化功能、支援行銷團隊制定精準策略。 整合生成式 AI 應用: 導入並開發生成式 AI 功能，例如自動化產出運動新聞與球員動態，豐富產品內容。 提升內部數據工作流程效率: 運用 AI 技術優化資料處理、分析與建模的流程，提升團隊整體開發效率。

為什麼大家喜歡在 RichWell Co.Ltd. 上班？ 1.彈性上班-早上不趕打卡，想多睡一點、避開通勤人潮都OK。2.特休多多-不用等滿一年就能休假，我們比法規更大方，放假就是要爽爽的。3.獎金福利讚 年終、績效獎金該有的都有，努力絕對不白費。4.生日小驚喜，公司記得你的每個重要時刻。5.定期聚餐/Team Building 不只是工作夥伴，更是一起成長的戰友，吃吃喝喝感情更緊密。6.技術課、內部分享會，想學什麼我們都支持，讓你持續進化不退化！ Key Responsibilities for Data Analyst: Responsible for gathering data from various sources, including internal databases, user interactions third-party APIs, ensuring data accuracy and integrity.Perform exploratory and statistical analysis to identify trends, patterns, and correlations in large datasets.Create clear, compelling reports and dashboards to communicate insights of the product to stakeholders.Work closely with product and engineering teams to define key performance indicators (KPIs) and track product performance.Provide recommendations to improve software features, user engagement, and operational efficiency based on data findings.Implement processes to ensure data quality, consistency, and security, adhering to company policies and regulations.Conduct custom analyses to support strategic initiatives or respond to business questions.

Established in 1987 and headquartered in Taiwan, TSMC pioneered the pure-play foundry business model with an exclusive focus on manufacturing its customers’ products. As of 2024, TSMC serves more than 500 customers and manufactures over 11,000 products for high-performance computing, smartphones, the Internet of Things (IoT), automotive, and digital consumer electronics. It is the world’s largest provider of logic ICs, with an annual capacity of 16 million 12-inch equivalent wafers. TSMC operates fabs in Taiwan as well as manufacturing subsidiaries in Washington State, Japan and China, and the Company began construction on a specialty technology fab in Dresden, Germany, in 2024. In Arizona, TSMC is building three fabs, with the first starting 4nm production in 2025, the second by 2028, and the third by the end of the decade.Advanced Quality System Development (AQSD) is part of TSMC's Intelligent Manufacturing Center (IMC), responsible for three main directions: Use statistical methods and conditional monitoring to inspect fab control charts, providing early warning of abnormal product yields; offer a fab defense system settings comparison platform to check that machines meet the internal control anomaly conditions set by the engineering department.Develop online fab analysis platforms through Big Data, Machine Learning, and Deep Learning techniques for online yield/defect analysis to ensure the quality of wafers produced in the fab.Develop systems and provide logical operations to control and implement escape mechanisms for material batch changes, measurement stages, and inspection stages, ensuring quality and optimizing product sampling rules. Responsibilities: Apply machine learning/optimization algorithm to build up models for optimizing semiconductor production.Develop analysis and optimization methods to enhance product quality, increase tool productivity and improve people productivity.Design, develop and test prediction models with real applications in manufacturing.Build a flexible framework to speed up the development process of AI models, with a focus on Large Language Models (LLMs) serving.Turn exciting AI prototypes/ideas into products, leveraging LLMs and other advanced AI technologies.Develop next-generation AI backend systems related to large-scale real-time data access, collection, analytics and monitoring.Establish and maintain MLops processes and tools, including model deployment, monitoring, and automation.Continuously improve the quality of AI production systems, particularly those utilizing LLMs.

* Salary will be commensurate with experience *We are looking for bright engineers who enjoy systems level programming, network devices and distributed systems. The role is that of a hands-on technical Date Plane Software Engineer to implement 4G/5G Interworking Network functions for the Ataya solution. You will translate customer features into optimized and scalable network function implementationTasks include: Development of data plane features such as rate limiting by service data flow, policy routing, traffic accountingEnhance resiliency of data plane with improvement in fault discovery and service recoveryProfiling functions and implementations to ensure performance and scalability Verification of own changes to software at component and system level and contribute to automated code coverage in continuous integration

At SWAG Live's data team, our mission is to democratize data by building a self-service data platform. We aim to empower internal teams to access, interpret, and derive valuable insights from data effectively.As a Data Scientist in this role, you will collaborate with a multidisciplinary team of engineers and analysts to tackle diverse challenges using quantitative techniques such as statistical analysis and machine learning. You will work with large, complex event-based datasets, conduct exploratory data analysis (EDA), define requirements, and develop deploy models. We are particularly seeking a data scientist with experience in building customized recommendation models and a strong interest in product-focused machine learning development. Responsibilities Leverage state-of-the-art algorithms to build fully customized recommenders and other growth models.Design, deploy and maintain all components necessary for modeling, including feature engineering, automatic model training tuning and engineering toolchains.Create a comprehensive monitoring framework to evaluate model performance and provide actionable insights to drive business growth, focusing on awareness conversion and transactions.Understand stakeholder business requirements and design end-to-end machine learning/AI solutions that are effective, practical, and robust in addressing business challenges.Collaborate closely with data engineers and backend engineers to develop scalable systems.Communicate efficiently with cross-functional teams, promote the implementation of strategic applications, and drive continuous optimization.

WorldQuant develops and deploys systematic financial strategies across a broad range of asset classes and global markets. We seek to produce high-quality predictive signals (alphas) through our proprietary research platform to employ financial strategies focused on market inefficiencies. Our teams work collaboratively to drive the production of alphas and financial strategies – the foundation of a balanced, global investment platform. WorldQuant is built on a culture that pairs academic sensibility with accountability for results. Employees are encouraged to think openly about problems, balancing intellectualism and practicality. Excellent ideas come from anyone, anywhere. Employees are encouraged to challenge conventional thinking and possess an attitude of continuous improvement. Our goal is to hire the best and the brightest. We value intellectual horsepower first and foremost, and people who demonstrate an outstanding talent. There is no roadmap to future success, so we need people who can help us build it. The Role: We are seeking an exceptionally talented data scientist with strong modeling and programming skills to join our team. In this role, you will work closely with data science team and technologists across the firm to develop appropriate features and metrics for data processing. Perform analysis and generate models of financial datasets using machine learning techniques Process, clean and verify the integrity of unstructured data and turn data into valuable insights Develop and create data that seek to predict the movement of financial market Transfer data into internal infrastructure applying variety of algorithmic techniques What You’ll Bring: Have a Master’s degree or higher from a leading university in Computer Science, Electrical Engineering or other related areas Good academic record Familiar with modeling, data structures, algorithms and optimizations Strong knowledge of machine/deep learning algorithms Proficient in programming languages of both C++ and Python Possess good communication and presentation skills in English Ability to work independently and as member of a team Research scientist mindset: deep thinker, creative, strong work ethic, persevering, smart a self-starter Detail oriented and capable of multitasking and delivering in fast-paced work environment As a plus: While not mandatory, a strong interest in financial markets will definitely be beneficial Participant of ACM-ICPC By submitting this application, you acknowledge and consent to terms of the WorldQuant Privacy Policy. The privacy policy offers an explanation of how and why your data will be collected, how it will be used and disclosed, how it will be retained and secured, and what legal rights are associated with that data (including the rights of access, correction, and deletion). The policy also describes legal and contractual limitations on these rights. The specific rights and obligations of individuals living and working in different areas may vary by jurisdiction. Copyright © 2025 WorldQuant, LLC. All Rights Reserved.WorldQuant is an equal opportunity employer and does not discriminate in hiring on the basis of race, color, creed, religion, sex, sexual orientation or preference, age, marital status, citizenship, national origin, disability, military status, genetic predisposition or carrier status, or any other protected characteristic as established by applicable law.

DescriptionAt SWAG Live, data is the backbone of how we innovate and grow. With our BI system established, the data team is now focused on building the next generation of platforms that power recommendation systems, AI agents, AI pipelines, and campaign-facing services—as well as external-facing data services that enhance our products. We are seeking a Data Engineer who will design and operate the infrastructure that makes these AI-driven initiatives possible. Your work will center on building cost-efficient, reliable, and maintainable systems that deliver measurable business value. You’ll collaborate closely with data scientists and product teams to turn models into production-ready services, enabling personalization, campaign optimization, and intelligent product features. This is a role for engineers who want to shape the future of applied AI while keeping efficiency at the core. Responsibilities Build and maintain cost-efficient data pipelines and warehouses that power analytical tool, recommendation systems, AI agents, and campaign-facing services.Develop data services that integrate with external products, ensuring reliability, maintainability, and clear SLAs.Optimize queries, schemas, and storage to maximize performance and minimize cost across transactional and analytical workloads.Implement and operate event-driven streaming architectures for real-time personalization and campaign insights.Collaborate with data scientists and product teams to move AI models from experimentation to production.Create internal tools and frameworks that accelerate the productivity of analysts, scientists, and engineers.Ensure data quality, governance, and observability across pipelines and services.