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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。

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。

1.開發深度學習/機器學習模型，或運用生成式AI(GenAI)、自然語言處理(NLP)、電腦視覺(CV)...等工具，協助優化公司內現有工作流程，提升作業效率和品質。2.跨單位合作，依據使用者提出之應用場景和業務痛點，規劃AI解決方案或定義分析命題，藉由生成式AI/深度學習/機器學習/統計分析等方法解決商業問題。3.協助MLOps工具之導入規劃、評估、測試、執行...等任務。4.協助公司發展AI治理，建立評測工具與方法，並制定規範及作業流程。5.研究如何將AI/深度學習/機器學習等技術應用於公司各式業務場景，以達成推展業務、找出潛在商機、降低風險或成本、提升服務品質等目標。

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.

【職務說明 What will you do】1. 生成式AI、機器學習、深度學習或統計分析模型專案開發。2. 配合數據技術發展目標，研究與實作可落地應用之新型態數據模型技術，包含大語言模型(LLM)、機器學習(ML)、深度學習(DL)等。3. 推動業務場景AI賦能，建立GenAI技術輔助各類型銀行需求使用，包含RAG架構流程、向量知識庫取用、Prompt Eng.設計等。4. 協助數據轉型，規劃從需求痛點到落地應用之end-to-end的數據解決方案。5. 當負責之數據服務被設定為"不能中斷"之服務等級，則需配合維運團隊於非上班時段on call以便即時處理問題，確保服務穩定。

About the roleWe’re looking for a Data Scientist to turn data into insights and production-ready solutions. You’ll partner with product, engineering, and business teams to frame problems, build models and analyses, and translate results into measurable impact.What you’ll do- Identify and integrate new internal or external datasets to enhance product capabilities and business insights.- Collaborate with engineering teams to support the development and deployment of scalable data products.- Conduct analytical experiments to address complex problems across different domains and industries.- Source and collect large volumes of structured and unstructured data based on client or business needs.- Develop and apply algorithms and models to extract value from big data, including error analysis and performance tuning.- Clean, validate, and transform data to ensure consistency, accuracy, and usability across pipelines.- Analyze data for trends and patterns, and translate findings into actionable insights with clear business objectives.

Minimum qualifications: Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field. 2 years of experience with analysis applications (extracting insights, performing statistical analysis, or solving business problems), and coding (Python, R, SQL) (or experience with a Master's degree). Preferred qualifications: Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field. 3 years of work experience with analysis applications (extracting insights, performing statistical analysis, or solving business problems), and coding (Python, R, SQL). 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. To accelerate the growth and market leadership of Enterprise Buying Platforms (DV360 and SA360) by answering critical business questions and delivering actionable, data-driven insights that inform product and commercial strategy. The Enterprise Platform Data Science Team provides quantitative support, market understanding and a strategic perspective to our partners throughout the organization, in close collaboration with the Ads and Commerce Finance team.Responsibilities Execute defined, moderately difficult investigative tasks under guidance from the manager or executive team member/team lead. For straightforward problems, execute end-to-end analysis with minimal guidance. Manage workload to reflect the priorities set by the team, work towards a timeline, and communicate slippage. Select appropriate approaches from clear options to address technical challenges under some guidance from managers or executive team members. Plan out analyses (as opposed to trial and error approach). Break down broader tasks into components and anticipate complexities/blockers. 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. 5 years of work experience with analysis applications (extracting insights, performing statistical analysis, or solving business problems), and coding (Python, R, SQL) (or 2 years of work experience with a Master's degree). Preferred qualifications: Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field. 5 years of experience with analysis applications (extracting insights, performing statistical analysis, or solving business problems), and coding (Python, R, SQL). Experience with developing one or more deep learning models for business impact, and experience debugging throughput and latency issues in AI. Experience managing large-scale data transformation pipelines for batch inference of ML models. 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. In this role, you will partner with teams across the company to apply Google’s best Data Science techniques to Google’s biggest enterprise opportunities. You will partner with Research, Core Enterprise ML and Machine Learning (ML) Infrastructure teams to build solutions for the enterprise.The Googler Technology and Engineering (GTE) Data Science team's mission is to transform Google Enterprise business operations, supply chain, IT support and internal tooling with Artificial Intelligence (AI) and advanced analytics, enable operations and product teams to succeed in their advanced analytics projects through the use of differing engagement models, ranging from consulting to productionizing and deploying models. Build cross-functional services for use across Corp Engineering, and educate product teams on advanced analytics and ML.Responsibilities Define and report Key Performance Indicators and launch impact as part of regular business reviews with the cross-functional and cross-organizational leadership team. Work with PM, User Experience (UX), and Engineering to contribute to metric-backed annual OKR setting. Come up with hypothesis to enhance performance of AI products on offline and online metrics through research on techniques around prompt engineering, RAG, supervised finetuning, in-context learning, dataset augmentation, tool-calling efficacy, planning capabilities and feedback loop with reinforcement learning. Design and develop ML strategies for data enrichment such as autoencoder based latent variables, complex heuristics, etc. Evolve variance reduction and simulation strategies to increase reliability of experiments with small sample sizes. Convert business problems into unsupervised and supervised ML modeling problems, build these model prototypes from scratch to justify business impact hypothesis. 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. 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.