得物大模型平台数据架构师（风控方向）
社招全职5年以上风控类地点：上海 | 杭州状态：招聘
工作描述
任职要求 1. 基础要求 - 本科及以上学历，计算机科学、数据科学、统计学、数学等相关专业，5年以上互联网大数据相关工作经验，具备电商或风控行业大数据架构设计经验者优先。 - 精通大数据技术栈，熟练掌握Hadoop、Spark、Flink、Hive、HBase等主流组件的原理与应用，能独立完成大规模数据平台的设计与搭建。 - 具备大模型相关数据支撑实践经验，熟悉大模型训练/微调的数据准备流程，了解向量数据库（Milvus/Weaviate等）、RAG技术及Agent框架（LangChain等）的应用逻辑。 - 熟练掌握Python/Java/Scala中至少一种编程语言，具备扎实的代码开发能力和工程化思维，能独立完成数据处理模块、数据接口的开发与优化。 - 具备敏锐的业务洞察力，能从复杂的风控场景中提炼数据需求，具备端到端数据解决方案设计与落地能力。 - 具备优秀的跨团队沟通协调能力、问题解决能力及抗压能力，能高效推动跨部门项目落…
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包括英文材料
学历+
数据科学+
https://roadmap.sh/ai-data-scientist
Step by step roadmap guide to becoming an AI and Data Scientist
大数据+
https://www.youtube.com/watch?v=bAyrObl7TYE
https://www.youtube.com/watch?v=H4bf_uuMC-g
With all this talk of Big Data, we got Rebecca Tickle to explain just what makes data into Big Data.
系统设计+
https://roadmap.sh/system-design
Everything you need to know about designing large scale systems.
https://www.youtube.com/watch?v=F2FmTdLtb_4
This complete system design tutorial covers scalability, reliability, data handling, and high-level architecture with clear explanations, real-world examples, and practical strategies.
Hadoop+
https://www.runoob.com/w3cnote/hadoop-tutorial.html
Hadoop 为庞大的计算机集群提供可靠的、可伸缩的应用层计算和存储支持，它允许使用简单的编程模型跨计算机群集分布式处理大型数据集，并且支持在单台计算机到几千台计算机之间进行扩展。
[英文] Hadoop Tutorial
https://www.tutorialspoint.com/hadoop/index.htm
Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models.
Spark+
[英文] Learning Spark Book
https://pages.databricks.com/rs/094-YMS-629/images/LearningSpark2.0.pdf
This new edition has been updated to reflect Apache Spark’s evolution through Spark 2.x and Spark 3.0, including its expanded ecosystem of built-in and external data sources, machine learning, and streaming technologies with which Spark is tightly integrated.
Flink+
https://nightlies.apache.org/flink/flink-docs-release-2.0/docs/learn-flink/overview/
This training presents an introduction to Apache Flink that includes just enough to get you started writing scalable streaming ETL, analytics, and event-driven applications, while leaving out a lot of (ultimately important) details.
https://www.youtube.com/watch?v=WajYe9iA2Uk&list=PLa7VYi0yPIH2GTo3vRtX8w9tgNTTyYSux
Today’s businesses are increasingly software-defined, and their business processes are being automated. Whether it’s orders and shipments, or downloads and clicks, business events can always be streamed. Flink can be used to manipulate, process, and react to these streaming events as they occur.
Hive+
[英文] Hive Tutorial
https://www.tutorialspoint.com/hive/index.htm
Hive is a data warehouse infrastructure tool to process structured data in Hadoop. It resides on top of Hadoop to summarize Big Data, and makes querying and analyzing easy.
https://www.youtube.com/watch?v=D4HqQ8-Ja9Y
HBase+
[英文] HBase Tutorial
https://www.tutorialspoint.com/hbase/index.htm
HBase is a data model that is similar to Google's big table designed to provide quick random access to huge amounts of structured data. This tutorial provides an introduction to HBase, the procedures to set up HBase on Hadoop File Systems, and ways to interact with HBase shell.
大模型+
https://www.youtube.com/watch?v=xZDB1naRUlk
You will build projects with LLMs that will enable you to create dynamic interfaces, interact with vast amounts of text data, and even empower LLMs with the capability to browse the internet for research papers.
https://www.youtube.com/watch?v=zjkBMFhNj_g
Milvus+
[英文] Tutorials Overview
https://milvus.io/docs/tutorials-overview.md
This page provides a list of tutorials for you to interact with Milvus.
https://www.baeldung.com/milvus-tutorial-intro
In this tutorial, we’ll explore Milvus, a highly scalable open-source vector database.
https://www.youtube.com/watch?v=7ejr_ZzU9jw
Discover the power of Milvus, an open-source vector database revolutionizing AI applications.
https://www.youtube.com/watch?v=Yhv19le0sBw
Vector databases have been trending recently as they power modern search, recommendations, and AI-driven applications.
RAG+
https://www.youtube.com/watch?v=sVcwVQRHIc8
Learn how to implement RAG (Retrieval Augmented Generation) from scratch, straight from a LangChain software engineer.
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