阿里云阿里云智能-大数据&AI解决方案架构师-北京&杭州
社招全职5年以上云智能集团地点:北京 | 杭州状态:招聘
任职要求
1. 5年以上企业级大数据产品研发、产品商业化、售前管理的相关工作经验 ,在面向客户维度的产品决策、执行和愿景规划上有全面把控能力,推动客户应用阿里云大数据产品。 有面向汽车、金融行业的相关岗位云厂商工作经验优先; 2. 有相关行业数据应用能力,对特定行业大数据应用与落地有实践经验,能够挖掘大数据应用的详细业务场景与推动行业数据价值放大; 3. 业务协同能力,具备良好的团队沟通协同能力,能推动多业务、角色的大型项目顺利推进与交付,结果导向带领团队拿到业务结果; 4. 计算机、统计、数学、信息技术等相关专业(统计学,机器学习,建模,数据分析与挖掘功底扎实),本科以上学历,有相关行业Data+AI相关从业背景优先 ; 5. 具有数据仓库、数据建模、数据治理等相关项目经验,熟练掌握至少一种分布式计算框架,如Hadoop、Spark、Flink、Paimon、ES等,并理解其架构和工作原理 6. 了解并掌握机器学习 (ML)、深度学习 (DL)、自然语言处理 (NLP)、计算机视觉(CV)的核心概念,如分类、回归、聚类、生成模型等概念; 7. 熟悉行业中Data+Al的结合场景与典型案例,了解相应领域的挑战、客户价值及大致的技术实现方式与原理。 8.熟悉云厂商&头部大数据公司在Data+Al领域的商业模式、产品方案、技术及行业优劣等 9. 具备Data+Al实操能力,理解数据处理框架 (PySpark/Ray等)与AI工程的协作模式,能够使用灵码、Cursor、Cline等工具 10. 需具备良好的沟通能力,具备英语听说读写能力优秀者优先。
工作职责
•主导大数据和AI产品解决方案的开发和标准化工作,负责产品从售前到交付的全流程解决方案支撑; •熟悉并了解行业典型大数据&AI方案,提炼行业大数据&AI典型产品场景,总结并推广行业打法和解决方案; •基于阿里云大数据&AI产品能力,协助客户进行产品部署与实施,通过不同大数据行业解决方案解决客户大数据场景中遇到的问题; •负责输出整体解决方案架构设计文档,管理总体技术方案的变更,并根据行业洞察中发现的客户需求迭代方案; •与业务及产研、交付团队共同推进标杆客户,并且作为产品解决方案的竞争力负责人,能够影响业内公司的关键决策; •赋能与支持阿里云的业务团队拿下市场份额,并且对产品的增长负责; •识别和反馈行业共性需求,推动云产品大数据&AI能力提升,打造业内有竞争力的大数据&AI产品 。
包括英文材料
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.
机器学习+
https://www.youtube.com/watch?v=0oyDqO8PjIg
Learn about machine learning and AI with this comprehensive 11-hour course from @LunarTech_ai.
https://www.youtube.com/watch?v=i_LwzRVP7bg
Learn Machine Learning in a way that is accessible to absolute beginners.
https://www.youtube.com/watch?v=NWONeJKn6kc
Learn the theory and practical application of machine learning concepts in this comprehensive course for beginners.
https://www.youtube.com/watch?v=PcbuKRNtCUc
Learn about all the most important concepts and terms related to machine learning and AI.
数据分析+
[英文] Data Analyst Roadmap
https://roadmap.sh/data-analyst
Step by step guide to becoming an Data Analyst in 2025
学历+
数据仓库+
https://www.youtube.com/watch?v=9GVqKuTVANE
From Zero to Data Warehouse Hero: A Full SQL Project Walkthrough and Real Industry Experience!
https://www.youtube.com/watch?v=k4tK2ttdSDg
数据治理+
https://www.ibm.com/think/topics/data-governance
Data governance is the data management discipline that focuses on the quality, security and availability of an organization’s data.
https://www.youtube.com/watch?v=uPsUjKLHLAg
Building data fabric eliminates the technological complexities of data governance so users can connect to the right data at the right time, regardless of where it resides.
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.
ElasticSearch+
https://www.youtube.com/watch?v=a4HBKEda_F8
Learn about Elasticsearch with this comprehensive course designed for beginners, featuring both theoretical concepts and hands-on applications using Python (though applicable to any programming language). The course is structured in two parts: first covering essential Elasticsearch fundamentals including index management, document storage, text analysis, pipeline creation, search functionality, and advanced features like semantic search and embeddings; followed by a practical section where you'll build a real-world website using Elasticsearch as a search engine, working with the Astronomy Picture of the Day (APOD) dataset to implement features such as data cleaning pipelines, tokenization, pagination, and aggregations.
深度学习+
https://d2l.ai/
Interactive deep learning book with code, math, and discussions.
NLP+
https://www.youtube.com/watch?v=fNxaJsNG3-s&list=PLQY2H8rRoyvzDbLUZkbudP-MFQZwNmU4S
Welcome to Zero to Hero for Natural Language Processing using TensorFlow!
https://www.youtube.com/watch?v=R-AG4-qZs1A&list=PLeo1K3hjS3uuvuAXhYjV2lMEShq2UYSwX
Natural Language Processing tutorial for beginners series in Python.
https://www.youtube.com/watch?v=rmVRLeJRkl4&list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4
The foundations of the effective modern methods for deep learning applied to NLP.
OpenCV+
https://learnopencv.com/getting-started-with-opencv/
At LearnOpenCV we are on a mission to educate the global workforce in computer vision and AI.
https://opencv.org/university/free-opencv-course/
This free OpenCV course will teach you how to manipulate images and videos, and detect objects and faces, among other exciting topics in just about 3 hours.
Ray+
https://github.com/ray-project/ray
Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
https://www.youtube.com/watch?v=FhXfEXUUQp0
In this video, I'll teach you everything you need to know about Apache Ray!
https://www.youtube.com/watch?v=fMiAyj2kgac
Using powerful machine learning algorithms is easy using Ray.io and Python.
https://www.youtube.com/watch?v=q_aTbb7XeL4
Parallel and Distributed computing sounds scary until you try this fantastic Python library.
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