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【關於我們 - Data Munger AI】 Data Munger 致力成為企業與實用 AI 解決方案間的最佳夥伴，從導入一路陪伴到落地。 在人人喊AI的時代下，真正為企業打造一個會持續進化的「AI 大腦」讓決策更快、流程更簡，讓人力價值最大化。 官網： https://datamunger.io/ 💼 工作職責 資料處理管道設計與開發 使用 Python 與 BigQuery SQL 為 AI 應用設計、開發及維護資料處理流程，確保機器學習模型可獲取高品質資料。ETL/ELT 工作流程構建 建立並優化 ETL/ELT 流程，確保資料自動化處理與數據一致性。RAG 系統實作 應用 RAG（檢索增強生成）技術，提升 AI 互動的準確性與上下文相關性。向量資料庫與查詢優化 優化向量資料庫（如 Pinecone、Qdrant）及 SQL/BigQuery 查詢效能，提升 AI 搜尋與回應速度。API 建構與維護 使用 Python 框架（FastAPI、Flask）建立並維護資料處理與 AI 服務 API。機器學習模型與資料管道部署 在雲端基礎架構（主要以 GCP 為主，並整合部分 AWS）上部署 ML 模型與資料管道，實踐 MLOps 原則以確保部署具備可重現性、擴展性及監控能力。跨部門協作與技術創新 與資料科學家、AI 研究人員及前端團隊協作，共同打造端對端 AI 產品，並持續導入最新技術以優化解決方案。

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.

・負責收集、清理、管理平台內外部數據，確保資料完整與正確，打造可供分析使用的數據集・監控數據質量與資料庫模型，持續優化數據處理流程・分析用戶行為（活躍、留存、付費）、市場活動成效及用戶來源渠道・應用統計、ML/DL 和生成式 AI 技術來建立使用者分類、詐欺偵測、情緒分析和其他目標的功能・以數據支持產品優化，協助設計及分析A/B測試・與其他產品開發團隊合作，了解他們的業務需求，如潛在用戶名單、用戶風險分析，制定並完成端到端分析，包括資料收集、分析、持續擴展交付和演示・使用Tableau/Power BI設計並維護BI報表，推動數據自助化・結合業界Knowhow與指標洞察，提出實際可行的解決方案

Minimum qualifications: Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field. 10 years of experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL), or 8 years of experience with a Master's degree. Preferred qualifications: Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field. 12 years of experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL). 5 years of experience in extracting and manipulating large datasets and designing ETL flows. About the jobHelp serve Google's worldwide user base of more than a billion people. Data Scientists provide quantitative support, market understanding and a strategic perspective to our partners throughout the organization. As a data-loving member of the team, you serve as an analytics expert for your partners, using numbers to help them make better decisions. You will weave stories with meaningful insight from data. You'll make critical recommendations for your fellow Googlers in Engineering and Product Management. You relish tallying up the numbers one minute and communicating your findings to a team leader the next. The Business Platform for Sales and Support (BPSS) team is part of Enterprise Platforms and Ecosystems (EPE) within Corporate Engineering (CorpEng), with a mission to build products for Google's internal contact center business for sales and support. We are a fast-moving, high-impact team that operates like a startup, with the resources of Google behind us. As a Data Scientist on this team, you will work closely with product and engineering teams throughout the entire development process to help build and shape next-generation GenAI products for our users.Responsibilities Define, own, and evolve product success metrics, as well as report, analyze, and forecast key product trends to make recommendations for improvement. Perform data exploration to understand user behavior and identify opportunities for improving products. Apply technical expertise in observational data analysis, modeling, and causal inference to answer product questions. Lead the design, analysis, and interpretation of product experiments to measure the causal effects of product changes. Partner with Product, Engineering, and other cross-functional teams to influence, prioritize, and support product strategy. This involves framing and solving ambiguous business problems, acting as a thought partner, influencing a wide range of product and engineering stakeholders. Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.

Minimum qualifications: Master's degree in Statistics or Economics, a related field, or equivalent practical experience. 8 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of work experience with a PhD degree. Experience with statistical data analysis such as linear models, multivariate analysis, causal inference, or sampling methods. Experience with statistical software (e.g., SQL, R, Python, MATLAB, pandas) and database languages along with Statistical Analysis, Modeling and Inference. Preferred qualifications: Experience translating analysis results into business recommendations. Experience understanding potential outcomes framework and with causal inference methods (e.g., split-testing, instrumental variables, difference-in-difference methods, fixed effects regression, panel data models, regression discontinuity, matching estimators). Experience selecting tools to solve data analysis issues. Experience articulating business questions and using data to find a solution. Knowledge of structural econometric methods. About the jobAt Google, data drives all of our decision-making. Quantitative Analysts work all across the organization to help shape Google's business and technical strategies by processing, analyzing and interpreting huge data sets. Using analytical excellence and statistical methods, you mine through data to identify opportunities for Google and our clients to operate more efficiently, from enhancing advertising efficacy to network infrastructure optimization to studying user behavior. As an analyst, you do more than just crunch the numbers. You work with Engineers, Product Managers, Sales Associates and Marketing teams to adjust Google's practices according to your findings. Identifying the problem is only half the job; you also figure out the solution. Responsibilities Interact cross-functionally with a variety of leaders and teams, and work with Engineers and Product Managers to identify opportunities for design and to assess improvements for advertising measurement products. Collaborate with teams to define questions about advertising effectiveness, incrementality assessment, the impact of privacy, user behavior, brand building, bidding etc., and develop and implement quantitative methods to answer those questions. Work with large, complex data sets. Solve difficult, non-routine analysis problems, applying advanced analytical methods as needed. Conduct analyses that include data gathering and requirements specification, exploratory data analysis (EDA), model development, and delivery of results to business partners and executives. Build and prototype analysis pipelines iteratively to provide insights at scale. Develop knowledge of Google data structures, metrics, advocating for changes where needed for product development. Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.

Minimum qualifications: Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field. 8 years of work experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL), or 5 years work experience with a Master's degree. Preferred qualifications: Advanced degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field. Experience with conceptualizing and implementing scalable data pipelines. Experience with statistical packages (e.g., R, Python, etc.) to perform forecasting, segmentation, and classification. Knowledge of basic statistics and commonly used statistical methods (e.g., hypothesis testing, regression, cohort analysis, etc.). Strong data visualization skills, including building dashboards and visualizations for business reviews and executive-level presentations. About the jobHelp serve Google's worldwide user base of more than a billion people. Data Scientists provide quantitative support, market understanding and a strategic perspective to our partners throughout the organization. As a data-loving member of the team, you serve as an analytics expert for your partners, using numbers to help them make better decisions. You will weave stories with meaningful insight from data. You'll make critical recommendations for your fellow Googlers in Engineering and Product Management. You relish tallying up the numbers one minute and communicating your findings to a team leader the next. Developer and Sustainability team’s mission is to apply data science to enable Geo in driving developer growth and planetary sustainability. We enable driven decision making across the Geo Makers organization (made up of Geo Developer and Geo Sustainability areas) to influence strategy, drive impact and unlock sustainable product growth.Responsibilities Lead the transformation of our self-serve analytics platform by introducing new metrics and drill downs to assist with self-serve diagnostics at scale, and driving integrations with GenAI tooling (NotebookLM, SQLMiner) to scale insight generation capabilities. Utilize technology to address new problem areas with segmentation analysis, recommender systems and GenAI applications. Build self-serve tooling and dashboarding to enable data driven decision making across the organization at scale. Collaborate on in-depth analytical projects, uncover insights and evaluate headroom to drive product enhancements which improve our developer experience. Surface insights around emerging markets (India and other APAC countries) that help teams understand users and guide global data improvement strategy. Design, execute and analyze experiments to evaluate growth levers and regional launches. Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.

1.風險數據分析 ・蒐集並分析遊戲相關交易數據，偵測異常與高風險行為・建立可視化風險控管儀表板，提供同仁觀察及定位出風險點・建立並優化風險模型，包含詐欺偵測、異常遊戲行為分析 2.監控與報告・建立即時監控系統，追蹤玩家與交易動態，提供即時風險警示・定期產出風險控管報告，協助管理層決策 3.跨部門合作 ・支援營運、產品與客服團隊，提供數據洞察，降低業務風險・制定風險控管流程與防範措施・協助內部人員辨識風險與詐騙手法

國內最大化妝品使用心得及排行榜綜合網站@cosme Taiwan(前身UrCosme,2004年創建)在台營運20年，為國內消費者找尋化妝品使用心得及排行榜綜合資訊的主流網站，隸屬於日本上市公司集團istyle.Inc.。集結豐富的美妝產業資訊及美妝愛好者的行為資料，美妝行銷總研(CMRI)運用此資料庫，以多元觀點進行消費者洞察分享，旨在提供美妝業界趨勢分析洞察。本職缺內容如下： 【CMRI美妝行銷總研】商業數據分析師(BUSINESS ANALYST)1. 透過@cosme站資料庫進行資料分析與消費者洞察。2. 定期舉辦seminar，對外分享美妝產業年度趨勢。(CMRI詳情可參閱：https://cmri.itrue.com.tw)3. 透過DMP資料解析，協助各事業部門發展視覺化dashboard—coseek (https://www.coseek.com.tw)。4. 推進「行銷資料科學與預測建模」之相關專案。 5. 其他主管交辦事項。

As fraud schemes grow increasingly sophisticated, protecting users and their assets has become one of MoMo’s top priorities.The Product Analyst plays a frontline role in this mission: transforming data into protection, ensuring millions of users stay safe and confident when using MoMo.Why You’ll Love This JobJoin the core team of the User Trust Program, working alongside product, data, and AI experts to fight fraud through intelligent detection and prevention systems.Design the user experience in the most critical moments, when a user’s money or account security is at risk, to ensure they’re protected yet can recover their transactions smoothly.Every insight, every rule you propose can prevent millions of users from being scammed. This isn’t just a product role, it’s a social mission.Our VisionTo build MoMo as the most secure and trusted fintech platform in Vietnam, where every user can transact with confidence and trust our protection completely.Mô tả công việc1. Security Fraud AnalysisMonitor fraud cases and transactions to identify new or evolving fraud patterns.Conduct root-cause analysis and translate findings into prevention rules or AI model features;Partner with Risk Analyst and Data Scientist to validate rule effectiveness (precision, recall, false positive rate);Continuously evaluate and refine rules to balance fraud loss reduction and user friction;2. User Experience OwnershipOwn end-to-end user flow for fraud response (blocking, warning, education, recovery).Collaborate with UI/UX designers to create clear and empathetic in-app experiences that guide users during a fraud detection;Work with engineers to implement and test blocking/treatment logic;Define and track metrics to improve the user experience in fraud response flows, ensuring blocked users can recover transactions seamlessly (e.g., CS tickets for unlocks, recovery SLA, false-positive resolution rate);Collaborate with the internal task force team and other Business Units across MoMo to design, implement, and optimize rules and treatments that prevent fraud and protect users;3. Measurement ReportingTrack and report team KPIs such as Fraud User Rate, Fraud Loss Rate and Fraud Loss;Monitor fraud trends, treatment outcomes, and financial impact;Present actionable insights in weekly team meetings to inform prioritization and next-step decision;Yêu cầu công việcStrong analytical thinking and SQL proficiency (data analysis validation);Understanding of risk security in fintech context;Experience with A/B testing or rule-based experimentation;Ability to translate complex fraud signals into user-friendly UX flows;Collaboration mindset and ability to influence cross-functional partners;(Preferred) Background in product analytics, risk/fraud ops, or fintech product management.

【關於 Pinkoi Data Squad】我們是 Pinkoi 的資料團隊，其目標為打造有效的數據平台，同時確保資料的品質與穩定性，讓資料在 Pinkoi 發揮最大的價值。Data Engineer 將與 Business Analyst／ML Engineer 緊密合作，透過數據探索問題、規劃後續的 Data Pipeline（資料管線） 實作，並提供 End-to-end 的 Data Solution，以擴展公司各部門的數據分析能力，並完善整體商業智慧系統。你需要負責的工作內容與產品、行銷、廣告等分析師與商業團隊合作，理解需求並規劃合適的 Data Pipeline。設計、實作與維護 ETL/ELT 流程，確保資料品質、穩定性與新鮮度。建立與優化資料模型，支援報表、分析與機器學習等多種場景。持續迭代並優化資料基礎建設，提升可擴展性與維運效率。建立監控與告警機制，確保 Pipeline 成功率與資料 SLA。我們希望你有的經驗跟特質對 Big Data 和 Data-driven engineering 有極大的熱情。瞭解 ETL 或 ELT 的流程，並具備 Data Modeling 跟開發 Data Pipeline 的能力。具備與產品 / 行銷團隊合作的經驗，並可與商業分析師、資料工程師，以及機器學習工程師協同合作。樂於學習最新的技術，隨時充實自我的專業技能。對大型語言模型（LLM）保持開放與好奇，願意學習並嘗試將其應用於資料工程工作（如：資料清理、欄位補全、文件生成、自動化測試），以探索新工具如何提升效率與使用者體驗。應徵條件數學 / 統計 / 資訊 / 資料科學相關科系畢業，或具備同等專業能力。一年以上數據分析或數據工程相關工作經驗。熟悉 Python、Scala、Java 等任何一種程式語言，Pinkoi 主要用 Python。熟悉 GNU/Linux 系統，Pinkoi 用 Ubuntu。熟悉關聯式資料庫的運用，例如 MySQL。加分條件熟悉廣告歸因邏輯，或具備相關數據處理經驗。具備使用者行為數據處理經驗。具有操作或架設 BI 工具的相關經驗。如 Superset、Tableau、Looker Studio，Pinkoi 使用 Superset 和 Redash。具有分散式運算的相關知識，例如 Spark、Hadoop、Hive 等一種或多種相關技術，Pinkoi 主要用 PySpark。熟悉雲端服務，例如 AWS Athena、S3、Glue，GCP BigQuery 等，Pinkoi 主要用 AWS。了解 dbt 的基礎架構和應用方式，如建立資料模型、撰寫測試、建立巨集等等。有將 LLM 應用於資料工程場景的經驗，例如規則抽取、欄位對齊與補全、文件／Data Catalog 生成、資料品質檢查等。＃不用想了，趕快來當個 Pinkoi 人如果你有信心能夠勝任這份工作，歡迎提供你的個人履歷與小作業，即刻應徵！