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公司介紹 我們的客戶是一家長期深耕金融科技與數位創新的大型金融機構，近年積極投入資料平台與雲端架構升級，打造企業級數據基礎設施，支援數據分析、AI模型與多元金融場景。 團隊目前正推動 Modern Data Stack 與資料治理體系建設，導入雲端數據平台、資料血緣管理與自動化資料流程，讓數據能真正驅動產品決策與金融服務創新。 對資料工程師而言，這是一個能接觸 大規模資料、成熟商業場景與完整數據治理架構 的環境，能在穩定產業背景下，參與企業級資料平台的設計與落地。 工作內容 設計與維護 雲地混合（Hybrid Cloud）數據架構 建立與開發 大規模 Data Pipeline，處理結構化、半結構化與非結構化資料 建立資料處理流程的 自動化監控與維運機制 評估與導入 Modern Data Stack，提升資料治理、品質與血緣管理能力 使用資料治理與元資料管理工具，管理企業數據資產 維護與優化 Airflow 工作流程平台，支援資料科學與分析團隊的資料處理流程 設計部署與監控策略，確保資料平台穩定與高可用性 使用的技術 Python SQL Apache Airflow GCP（Composer / Dataflow） Terraform CI/CD Pipeline Data Governance / Data Lineage Modern Data Stack

我們正在尋找一位熟悉資料平台開發與維運的工程師，負責資料專案執行、ETL / Airflow 資料管線、資料庫查詢、排程任務與 production 資料系統的穩定運作。這個職位也會參與 AI-assisted Agent Development 的導入，包含 Text2SQL、資料查詢 Agent、SQL 輔助 Agent、資料品質檢查 Agent 與維運排查 Agent 等應用。你不需要一開始就是 AI Agent 專家，但需要具備紮實的 SQL、Python / Shell、Linux 與資料平台操作能力。若具備 DevOps、Production Operation 或系統維運管理經驗，將會是我們優先考慮的對象。 這個職位也會參與AI-assisted Agent Development的導入，包含Text2SQL、資料查詢Agent、SQL輔助Agent、資料品質檢查Agent與維運排查Agent等應用。 你不需要一開始就是AI Agent專家，但需要具備紮實的SQL、Python / Shell、Linux與資料平台操作能力。若具備DevOps、Production Operation或系統維運管理經驗，將會是我們優先考慮的對象。 主要工作內容 一、資料專案推進與執行 使用SQL、Python處理資料查詢、資料比對、報表與分析需求。開發、維護與優化ETL、Airflow DAG與資料管線。協助將資料需求轉換為資料處理流程、資料表設計或報表產出邏輯。與PM、QA、前後端工程師協作，釐清需求、排查問題並完成交付。撰寫必要的技術文件、資料處理說明與交接文件。 二、線上資料系統與管線維運管理 維護production data pipeline、排程任務、資料產出與資料平台服務的穩定運作。排查資料異常、ETL失敗、查詢效能問題與production pipeline問題。操作Linux，進行log查看、系統資源確認、服務狀態檢查與初步問題排查。撰寫Python / Shell Script，提升資料處理、監控檢查與維運自動化效率。參與系統維運、部署流程、排程監控、log分析、異常追蹤與維運流程改善。協助整理Runbook、SOP、異常處理紀錄與維運文件。 三、AI Agent / Text2SQL協作開發 參與Text2SQL、資料查詢Agent、SQL輔助Agent、資料品質檢查Agent、維運排查Agent等AI應用開發。協助AI功能所需的資料準備、資料處理、結果驗證與系統整合。使用Claude Code、Codex等AI工具輔助SQL、Python、Shell、ETL與文件產出。協助驗證AI產出的SQL、程式碼、查詢結果與排查建議。與資料科學、產品與工程團隊協作，將AI功能落地到資料平台或內部工具流程中。視專案需求，參與Google ADK或其他Agent framework的應用評估與開發。 工作亮點 AI Agent開發實踐中 團隊已實際使用Claude Code、Codex等AI工具輔助日常開發，並正在探索Text2SQL、資料查詢輔助、維運排查輔助、資料工程自動化與AI Agent開發等應用場景。 此職位將有機會參與資料平台相關AI Agent的設計與開發，例如資料查詢Agent、SQL輔助Agent、資料品質檢查Agent、維運排查Agent與文件產出Agent。 團隊也將視專案需求評估導入Google Agent Development Kit（Google ADK）等Agent開發框架，建立更結構化、可測試、可維護的AI Agent開發流程。 重視Production Operation與系統穩定性 我們希望工程師不只會寫資料處理邏輯，也能理解系統維運、部署流程、監控告警、log分析、排程狀態與production issue的處理流程。 具備DevOps、Production Operation或系統維運管理經驗者，將能更快參與資料平台穩定性改善、自動化部署、異常追蹤與監控流程優化。 完整資料平台實務場景 你會參與資料平台從資料專案執行、資料管線開發、上線、排程、監控、異常排查到維運改善的完整流程。 實際場景包含廣告數據、CDP、行銷分析、BI與production data pipeline。

・從資料庫中擷取並分析大型資料集・協助將模型部署至生產環境系統中，並確保其穩定性・運用資料探勘（Data Mining）與機器學習技術，設計並實作軟體系統與工具，以達成內部流程自動化・與跨部門團隊合作，提供技術見解，協助團隊了解機器學習的應用與限制

[本職缺僅接受104網站投遞] 請至統一超商104招募頁面投遞個人履歷表，路徑指引:https://www.104.com.tw/job/8p2a1?jobsource=google【工作內容】1. 設計資料模型與數據建模轉化 (Data Modeling)：主導 數據架構規劃與 Medallion 模型設計，定義企業級 Golden Table 規範，確保模型具備高度擴展性以支援 AI/ML 應用。2. BI 報表與儀表板開發：根據專案需求，在 Databricks 上以 Python / SQL 建置視覺化儀表板，並主動優化報表讀取效能。3. CDP 數據維護：主導 CDP 核心邏輯設計，包含用戶 ID Mapping、行為標籤化及多通路歸因邏輯，建構單一用戶視角 (Single Customer View)。4. 數據品質監控：維護數據管道 (Pipeline) 的日常運作，執行數據驗證，確保報表數據一致性。5. 跨部門協作／角色：參與業務需求討論，將「商業問題」轉化為「數據需求」，並協助資料科學家準備模型訓練所需的特徵資料。6. 推動資料治理：制定並推動資料治理政策與標準，進行元數據管理/權限設計/血緣管理，確保資料資產的管理做法符合最佳實踐。【專業能力】【Required】1. Python：能運用物件導向 (OOP) 與 Functional Programming 設計模式編寫具備可擴展性、易於維護且可測試 (Testable) 的程式碼。2. SQL：熟悉 Join 邏輯、Window Functions 以及效能調優。3. 數據模型經驗：具備 Data Modeling (Star Schema / Snowflake Schema) 實務設計經驗。4. Databricks / Spark 實務：Databricks 平台操作與實務經驗，能在 Notebook 或 Workflows 中管理資料流程，並深度理解資料分層（Bronze/Silver/Gold）的意義，能根據資料成熟度將資料放置在正確的資料分層，並從中運用 PySpark 或 Spark SQL 處理大規模資料轉換。5. 自動化調度：具備至少一種 排程編排工具（Databricks Workflows 或 Airflow）的基礎經驗，能編排並優化複雜的任務排程。6. 視覺化工具：具備至少一種 BI 工具經驗（Databricks Dashboard / Power BI / Tableau / Looker），具備基礎的視覺化設計美感，知道如何清楚呈現 KPI。【加分條件】【Nice to have】1. 零售或電商領域知識：了解零售／電商常見指標（如：AOV、轉化率、歸因模型）。2. 數據管理經驗：曾接觸元數據管理與數據治理相關經驗。3. 雲端經驗：具備至少一種 AWS / GCP / Azure 雲端平台操作基礎。4. CICD：具備 Databricks Asset Bundle / GitHub Actions 經驗，能將數據管道的部署自動化，並導入數據品質監控。▲uniopen團隊在做什麼?https://blog.104.com.tw/the-innovative-integration-of-the-uniopen-team/

【關於這個職位】你將設計與維護資料管線（data pipelines）、建立同時供產品與 AI 使用的資料模型，並打造讓 AI Agent 能可靠查詢、推理與操作公司資料的基礎架構。工作範圍涵蓋資料擷取、品質監控、語意層（semantic layer）設計，以及 AI Agent 的資料介面。你的主要重點會放在資料層本身 —— 包括資料管線、資料模型、品質監控與語意基礎設施。但這個角色也會深入產品與機器學習領域，例如：- 建立 AI 產品功能- 探索與驗證 ML 方法- 將模型從研究階段推向正式上線目前我們尚未建立完整的 ML pipeline，因此我們希望找到一位足夠好奇、願意探索可能性、找出適合我們問題的方法，並協助從零開始建立這項能力的人。不是所有嘗試第一次都會成功，而這完全沒問題。我們正在尋找一位能高度自主工作的人：能定義問題、評估方案、做出取捨決策，並真正把產品交付出去。【我們正在打造什麼】大多數公司都會在產品資料庫、分析工具與手動報表之間累積大量資料，最終卻面臨：- 指標定義不一致- 商業邏輯不清楚- 重複的人工流程- 關鍵知識只存在於少數人的腦中而我們正在解決這個問題。我們的工作橫跨四個層次：1. 可信資料（Trusted Data）：擷取、清理、監控資料，讓資料可靠可信。2. 定義完善的資料（Defined Data）：將原始資料轉換成有良好文件化的資料集、指標與商業定義。3. 商業脈絡（Business Context）：保存讓資料有意義的領域知識、規則與假設。4. AI 工作流程（AI Workflows）：將上述所有內容整合進 AI Agent、自助分析與實際可用的自動化流程中。你將主要參與第 1–3 層，並隨著對業務理解增加，逐步深入第 4 層。AI 並非只是概念驗證（demo）而已。我們正在正式推出 AI Agents，用來：- 回答商業問題- 自動化報表- 協助營運決策【主要職責】- 設計、建立與維護批次與串流資料管線，來源包含：資料庫、API、雲端儲存、Log、Event Streams- 建立與維護資料模型（raw / cleaned / curated），供以下用途使用：產品功能、報表、分析、AI 系統- 定義指標邏輯、資料品質規則與使用文件，讓人類與 AI 都能正確且有信心地使用資料。- 建立與維護語意描述、metadata 與 context layer，使 AI Agent 不只理解資料結構，更理解資料意義。- 設計供 AI Agent 查詢的資料介面與 API，確保穩定性、可解釋性、結果正確性- 調查資料異常與不一致問題，找出根本原因，並建立可持續的預防與監控機制。- 與產品、商務與工程團隊合作，對齊資料定義並交付關鍵資料資產。- 建立或延伸 AI 產品功能，例如：Agent workflow、推薦邏輯、自動化報表- 研究、原型開發與評估 ML 方法，解決真實商業問題，並將有潛力的方法投入生產環境。由於我們正在建立這項能力，你也將參與定義它未來的樣貌。【必備資格】〔工程深度 Engineering Depth〕我們更重視你的思考方式，而不是你會哪些工具。你應該能從第一原理（first principles）出發設計資料系統，例如：1. 對儲存模型、索引設計、查詢最佳化與反正規化（denormalization）做合理取捨2. 理解分散式資料系統中的：一致性（consistency）、分區（partitioning）、故障模式（failure modes）、傳輸保證（delivery guarantees）3. 能分析效能瓶頸，例如：記憶體、concurrency、I/O、網路問題，而不是只靠猜測反覆重試。4. 能熟悉操作：雲端基礎架構、容器化工作負載、CI/CD pipeline〔AI 與 ML 能力〕1. 有為 AI / ML 準備資料的經驗，例如：feature store, semantic layer, prompt-driven data interface, AI agent data backend2. 理解 AI 系統如何消費與推理結構化資料，以及資料層不可靠時會造成哪些問題。3. 至少具備以下其中一項實務經驗：- 為 AI Agent 建立 data API- 設計 semantic / context layer- 支援 LLM-powered analytics- 推出 AI-enabled data product4. 能閱讀 ML 論文、評估方法，並自行實作可運作的解決方案。你不一定需要 ML 研究背景或發表過論文，但應該能從：「這是一個問題」走到「這是一個可運作的 prototype」〔判斷力與 Ownership〕1. 能在資訊不完整時有效工作：先定義可行版本、明確說明假設、持續迭代2. 能評估多種方案並解釋設計選擇背後的理由。3. 主動溝通：分享進度、提前提出阻礙、向非技術人員解釋技術決策4. 能主動發現下一個高價值任務，而不是等待指示。5. 願意在需要時支援相鄰領域：backend service、internal tooling、prototype 開發〔加分條件〕1. 有 modern data platform / lakehouse 架構經驗，例如：Databricks、Snowflake、BigQuery2. 熟悉 Spark、Delta Lake、dbt、Airflow，或類似 orchestration / transformation 工具3. 有資料觀測性（observability）、lineage、品質監控與 governance 經驗。4. 有推出以下產品的經驗：AI Agent、RAG 系統、LLM-powered application（且以結構化資料為基礎）5. 有新創公司或小型工程團隊工作經驗。

The MoMo Recommendation Platform is a complex system that powers personalized experiences for millions of users using a diverse range of technologies.We’re looking for a Senior Software Engineer with strong system thinking, architecture design skills, and a product mindset to help build the MLOps platform that transforms any AI/ML solutions into production-grade systems at scale.Mô tả công việcThink like a product engineer: you don’t just “code a solution” – you build a platform that empowers others to deliver intelligent sysDesign and develop a flexible platform that turns AI/ML solutions into production-ready systems: microservices, batch pipelines, or real-time APIsBuild infrastructure to support:Model training pipelinesPackaging deploymentServing rolloutMonitoring alertingCollaborate closely with Data Scientists, Business, and Product teams to deeply understand requirements and design adaptable, scalable solutionsIntegrate platform components into MoMo’s broader infrastructure: promotion engine, A/B testing, analytics, real-time scoring, etc.Yêu cầu công việcMust-Have5+ years of experience in software development, system architecture, or backend/platform engineeringProficiency in one or more of the following: Python, Bash, C++, JavaScript, Java, or GoStrong problem-solving skills and teamwork spiritExperience with:Platform Deployment: Kubernetes, Helm, Argo CD, Argo Rollouts, Docker, Google Cloud Platform (GCP) or Amazon Web Services (AWS)Serving APIs: FastAPI, gRPC, MLflow, KServe, custom logic services, REST APIsData Messaging: BigQuery, Redis, MongoDB, PostgreSQL, Oracle, MySQL, Kafka, Pub/SubOrchestration Workflow: Airflow, Argo WorkflowsCI/CD Monitoring: GitHub Actions, Prometheus, GrafanaData Sources: App event streams, relational databases, messaging systems, APIsSolid understanding of distributed systems and cloud-native architectureAbility to design systems that support diverse solution typesPlatform mindset: you build for stability, scalability, and long-term maintainabilityStrong communication and collaboration skills – able to work cross-functionally with Data Scientists, DevOps, and Product teamsNice-to-HaveExperience working with both AI/MLExperience scaling low-latency / real-time systemsFamiliarity with A/B testing, canary release, and shadow deployment strategiesProduct-oriented mindset: you build systems that others can easily adopt and extend

In a Financial Services context, data carries weight in financial accuracy and regulatory compliance: reconciliation, audit trail, PII handling, regulatory reporting. The models you build are not only the source for dashboards and reports, but also the semantic foundation for internal AI products — specifically a natural-language-to-SQL data assistant. A clearly defined, well-documented model layer is the prerequisite for the agent to generate accurate SQL.Mô tả công việcDevelop and maintain data transformation models in a layered architecture (staging → intermediate → marts), applying dimensional modeling (star schema, slowly changing dimensions) and medallion-style layering. Write SQL transformations optimized for the warehouse: understand materialization strategy (view vs table vs incremental), partitioning / clustering, and query cost — not just "correct" but "efficient". Build and maintain data quality tests (not-null, unique, relationship, accepted-values, freshness) and assertions; investigate root cause and resolve test failures. Document models, columns, and business logic as code (data dictionary, lineage) so both humans and the AI assistant can interpret them — documentation is part of the artifact, not an afterthought. Own the metrics / semantic layer for assigned domains: standardize metric definitions (e.g. disbursed amount, GMV, NPL, conversion rate) so each metric has a single source of truth, avoiding the "every report shows a different number" problem. Participate in code review via the Git/Gerrit workflow: follow branching, change-management, and CI conventions. Collaborate with DA, BA, Product, and Finance to translate business requirements into data models; work with DE on upstream data contracts. Monitor model/pipeline freshness and reliability; participate in data incident triage and contribute to data reliability governance initiatives. Yêu cầu công việcMust-have1–3 years working with dataStrong SQL: joins, window functions, CTEs, aggregation; able to read/write complex queries and reason about both correctness and performance.Solid grasp of data modeling fundamentals: fact vs dimension, grain, normalization vs denormalization, and when to use which.Version control with Git: branch, commit, merge/rebase, resolve conflicts; understands the code review workflow.Basic Python for data manipulation / scripting.Analytical mindset: able to decompose an ambiguous business question into a clear data structureNice to havedbt experience (models, tests, macros, snapshots, docs) — strong plus. Cloud data warehouse experience: BigQuery / Snowflake / Redshift / Databricks. Familiarity with orchestration (Airflow / Dagster) and the ELT paradigm. FS/fintech domain knowledge: lending, payments, banking, risk metrics. BI tools: Looker, Metabase, Superset, Power BI. Understanding of data governance, PII sensitivity, and regulatory reporting. Exposure to AI/LLM tooling for data (semantic/metric layers powering NL-to-SQL).

About the Role We are seeking a Data Scientist with deep expertise in both classical machine learning and Generative AI to drive digital transformation in the process industries, including energy, chemicals, manufacturing, and industrial automation. You will work on high-impact use cases such as predictive maintenance, process optimization, supply chain forecasting, and intelligent automation—leveraging massive industrial datasets, domain-specific knowledge, and cutting-edge AI/ML techniques. Drawing inspiration from the work done at AspenTech, Aveva, Siemens, C3.ai, and Palantir, you will lead the development and deployment of production-grade models that create measurable business value for mission-critical operations. Key Responsibilities ● Design, develop, and deploy advanced machine learning and deep learning models for predictive analytics, anomaly detection, optimization, and simulation in process industries. ● Lead the application of Generative AI (LLMs, foundation models) to use cases such as natural language querying of industrial data, automated reporting, operator assist, and control recommendations. ● Collaborate with SMEs, engineers, and product teams to define high-impact use cases grounded in operational and engineering realities. ● Work with large-scale industrial time-series, sensor, and historian data (e.g., OSIsoft PI, Aspen IP.21). ● Build and evaluate digital twins for industrial assets and processes, including physics-informed ML models. ● Apply techniques such as reinforcement learning, probabilistic modeling, and graph learning where applicable. ● Stay current with the GenAI and ML research landscape, and translate breakthroughs into practical solutions. ● Contribute to data pipelines, model monitoring, retraining strategies, and deployment using MLOps tools and platforms. ● Communicate results and insights clearly to both technical and non-technical stakeholders. Required Qualifications ● 1~3+ years of experience in applied data science, with a strong track record of delivering real-world ML/AI solutions. ● Deep understanding of supervised/unsupervised learning, time-series analysis, and generative models (e.g., transformers, diffusion models). ● Experience working with industrial datasets and process industry platforms such as Aveva, AspenTech, Honeywell, or Siemens Xcelerator. ● Proficiency with Python, ML libraries (e.g., scikit-learn, XGBoost, PyTorch, TensorFlow), and GenAI frameworks (e.g., LangChain, HuggingFace, OpenAI APIs). ● Familiarity with MLOps tooling such as MLflow, Airflow, SageMaker, or Azure ML. ● Strong analytical and problem-solving skills with the ability to work across messy and heterogeneous data sources. ● Solid grounding in statistics, optimization, and experimental design. ● Excellent communication and collaboration skills, with the ability to partner with engineers, domain experts, and executives. Preferred Qualifications ● Background in chemical, mechanical, or process engineering ● Experience with digital twins, control systems, or simulation tools (e.g., Aspen Plus, gPROMS). ● Knowledge of graph data models, semantic layers, or ontologies used in industrial contexts. ● Understanding of domain-specific regulatory, safety, and operational requirements. ● Experience with secure and scalable AI deployment in enterprise or OT/IT hybrid environments.

Minimum qualifications: Bachelor's degree in Computer Science, Electrical Engineering, a related technical field, or equivalent practical experience. 3 years of experience with interactions between hardware and software at the system level. Preferred qualifications: Master's degree or PhD in Computer Science, Electrical Engineering, a related technical field, or equivalent practical experience. Experience with liquid cooling technologies (e.g., DLC, sidecars) and their application in data center environments. Experience with server hardware from Dell (PowerEdge), HPE, and NVIDIA (HGX/NVL platforms). Experience with thermal modeling or Computational Fluid Dynamics (CFD) software to predict hotspot formation in high-density racks. Familiarity with high-power electrical standards and facility interfaces for liquid-cooled hardware. About the jobThe Google Cloud team helps companies, schools, and government seamlessly make the switch to Google products and supports them along the way. You listen to the customer and swiftly problem-solve technical issues to show how our products can make businesses more productive, collaborative, and innovative. You work closely with a cross-functional team of web developers and systems administrators, not to mention a variety of both regional and international customers. Your relationships with customers are crucial in helping Google grow its Cloud business and helping companies around the world innovate. 0Google is bringing our cloud anywhere with Google Distributed Cloud—in your data center, at the edge, and in the cloud.Google Distributed Cloud (GDC) is a portfolio of fully managed Hardware (HW) and Software (SW) solutions which extends Google Cloud’s infrastructure to the edge and to customers’ data centers. It is enabled by Anthos and is ideal for local data processing, edge-computing for latency-sensitive workloads, and for meeting sovereignty, strict data security, and privacy requirements.Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.Responsibilities Lead mechanical, thermal, and power design and validation for GDC rack infrastructure, accommodating next-generation advanced GPUs, including platforms such as NVIDIA Blackwell, Rubin, and future high-density AI accelerators. Analyze power consumption and thermal output of new components (e.g., servers, ToRs, switches, HSM, etc.) to determine impact on the existing rack designs, specifically addressing the shift to liquid-cooled requirements. Work closely with Original Equipment Manufacturer (OEMs), Product and Engineering teams to analyze their specific Hyperscale Graphics eXtension (HGX). Calculate rack power budgets and density limits, ensuring designs accommodate high-voltage requirements without exceeding infrastructure capacity. Model and validate airflow for mixed environments, including the integration of liquid-to-air sidecar heat exchangers. 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.

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.Technologists at WorldQuant research, design, code, test and deploy firmwide platforms and tooling while working collaboratively with researchers. Our environment is relaxed yet intellectually driven. We seek people who think in code and are motivated by being around like-minded people. The Role: As an NLP Data Engineer at WorldQuant, you will be at the heart of transforming unstructured text into actionable, high‑value insights that power quantitative investment strategies. This is a hands-on, engineering role where you’ll design, build, and scale the data pipelines that underpin our data research. This role is ideal for someone who loves building production systems, enjoys working deeply with text and large language models, and wants their engineering work to empower quantitative research at the firm. You’ll join a highly technical, collaborative environment where you work closely with Research and where your ideas can quickly translate into impact at scale. What You’ll Bring: BSc/M.Sc. from a leading university in Computer Science, Engineering, or related discipline 5 years of demonstrated experience programming scalable and robust software in Python Demonstrated experience building or maintaining data pipelines Basic knowledge of probability and statistical theory Experience working in Linux environments Experience with building and operating ML inference pipelines. Experience with using LLM for structured data extraction. Strong communication skills; ability to express complex concepts in simple terms Experience in the financial services industry is a big plus Knowledge of workflow scheduling techniques (e.g. Airflow) is a plus Prior experience working with text data in a data science/quantitative project environment What We Offer: Competitive and attractive compensation package with clear career road-map – where you feel challenged everyday We offer a strong culture of learning and development: training courses, library, speakers, share and learn events Learn from who sits next to you! Working in WQ you are surrounded by smart and talented people Premium Health Insurance and Employee Assistance Program Generous time-off policy, re-creation sabbatical leave (based on tenure), Trade Union benefits for staff and family Team building activities every month: Local engagement events – Employee clubs: football, ping-pong, badminton, yoga, running, PS5, movies, etc. Annual company trip and occasional global conferences – opportunity to travel and connect with our global teams Happy-hour with tea break, snacks and meals every day in the office! #LI-QM1By 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.