理想汽车热管理数据分析工程师 - 北京
社招全职3年以上汽车研发地点：北京状态：招聘
任职要求
1. 本科及以上学历，计算机、软件工程、数据科学等相关专业，3 年及以上机器学习数据工程或 MLOps 实战经验； 2. 精通 Python 与 SQL，熟练使用 Pandas、PySpark，理解 Spark 执行原理及性能调优； 3. 熟悉 Scikit-learn、TensorFlow 或 PyTorch 等机器学习框架的训练与服务化管线； 4. 掌握 Hive、Iceberg、Delta Lake 等数据仓库 / 数据湖技术，具备数据建模与存储优化能力； 5. 了解 Hadoop、Kafka、Airflow / Kubeflow、Docke…
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工作职责
1. 负责数据预处理与特征工程流水线的开发与维护（缺失值、异常值处理、标准化、编码等），保障模型训练与线上推理数据的一致性； 2. 与算法团队协同，持续优化训练数据集构建逻辑，支持模型迭代、测试流程； 3. 建立数据质量监控体系，定位并分析数据问题根因，提出改进方案并推动数据源或处理流程优化落地； 4. 优化数据仓库 / 数据湖存储架构及 Spark 计算资源配置，提升数据查询和模型训练效率； 5. 作为部门与公司级大数据平台的接口人，参与平台需求评审、接口设计与系统集成，输出技术文档与规范； 6. 协助平台团队进行作业调度、性能调优与监控告警策略设计，确保数据链路稳定高效； 7. 参与制定数据处理与 MLOps 开发规范，提升流程自动化水平与团队协作效率。
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
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.
Python+
https://liaoxuefeng.com/books/python/introduction/index.html
中文，免费，零起点，完整示例，基于最新的Python 3版本。
https://www.learnpython.org/
a free interactive Python tutorial for people who want to learn Python, fast.
https://www.youtube.com/watch?v=K5KVEU3aaeQ
Master Python from scratch 🚀 No fluff—just clear, practical coding skills to kickstart your journey!
https://www.youtube.com/watch?v=rfscVS0vtbw
This course will give you a full introduction into all of the core concepts in python.
SQL+
https://liaoxuefeng.com/books/sql/introduction/index.html
什么是SQL？简单地说，SQL就是访问和处理关系数据库的计算机标准语言。
https://sqlbolt.com/
Learn SQL with simple, interactive exercises.
https://www.youtube.com/watch?v=p3qvj9hO_Bo
In this video we will cover everything you need to know about SQL in only 60 minutes.
Pandas+
[英文] 10 minutes to pandas
https://pandas.pydata.org/docs/user_guide/10min.html
This is a short introduction to pandas, geared mainly for new users.
[英文] Cookbook - pandas
https://pandas.pydata.org/docs/user_guide/cookbook.html#cookbook
This is a repository for short and sweet examples and links for useful pandas recipes.
https://www.kaggle.com/learn/pandas
Solve short hands-on challenges to perfect your data manipulation skills.
https://www.youtube.com/watch?v=2uvysYbKdjM
I'm super excited for this one. We're doing another complete Python Pandas tutorial walkthrough.
https://www.youtube.com/watch?v=Mdq1WWSdUtw
Filtering, Joins, Indexing, Data Cleaning, Visualizations
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.
性能调优+
https://goperf.dev/
The Go App Optimization Guide is a series of in-depth, technical articles for developers who want to get more performance out of their Go code without relying on guesswork or cargo cult patterns.
https://web.dev/learn/performance
This course is designed for those new to web performance, a vital aspect of the user experience.
https://www.ibm.com/think/insights/application-performance-optimization
Application performance is not just a simple concern for most organizations; it’s a critical factor in their business’s success.
https://www.oreilly.com/library/view/optimizing-java/9781492039259/
Performance tuning is an experimental science, but that doesn’t mean engineers should resort to guesswork and folklore to get the job done.
Scikit-learn+
https://www.ibm.com/think/topics/scikit-learn
Scikit-learn, or sklearn, is an open source project and one of the most used machine learning (ML) libraries today.
https://www.youtube.com/watch?v=SIEaLBXr0rk
Today we to a crash course on Scikit-Learn, the go-to library in Python when it comes to traditional machine learning algorithms (i.e., not deep learning).
TensorFlow+
https://www.youtube.com/watch?v=tpCFfeUEGs8
Ready to learn the fundamentals of TensorFlow and deep learning with Python? Well, you’ve come to the right place.
https://www.youtube.com/watch?v=ZUKz4125WNI
This part continues right where part one left off so get that Google Colab window open and get ready to write plenty more TensorFlow code.
PyTorch+
https://datawhalechina.github.io/thorough-pytorch/
PyTorch是利用深度学习进行数据科学研究的重要工具，在灵活性、可读性和性能上都具备相当的优势，近年来已成为学术界实现深度学习算法最常用的框架。
https://www.youtube.com/watch?v=V_xro1bcAuA
Learn PyTorch for deep learning in this comprehensive course for beginners. PyTorch is a machine learning framework written in Python.
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
Iceberg+
https://iceberg.apache.org/spark-quickstart/
This guide will get you up and running with Apache Iceberg™ using Apache Spark™, including sample code to highlight some powerful features.
https://www.baeldung.com/apache-iceberg-intro
This tutorial will discuss Apache Iceberg, a popular open table format in today’s big data landscape.
https://www.youtube.com/watch?v=TsmhRZElPvM
You’ve probably heard about Apache Iceberg™—after all, it’s been getting a lot of buzz.
还有更多 •••
相关职位
校招热管理
1. 负责空调热管理智能算法的需求分析； 2. 负责空调热管理智能算法模型开发； 3. 负责空调热管理智能算法仿真环境搭建与维护； 4. 负责空调热管理智能算法集成落地； 5. 负责空调热管理大数据挖掘分析、优化算法； 6. 负责空调热管理算法问题跟踪、分析和解决。
上海
校招热管理
1. 负责空调热管理系统控制软件需求分析； 2. 负责空调热管理系统控制软件测试台架开发； 3. 负责空调热管理系统台架测试和实车测试，及测试用例开发； 4. 负责空调热管理系统的系统性能标定验证； 5. 负责空调热管理系统的系统及舒适性标定，制定舒适性标定流程； 6. 负责空调热管理系统的问题跟踪 分析和解决； 7. 负责空调热管理系统大数据挖掘 分析工作，优化系统功 性能表现； 8. 负责开通热管理性能标定成熟度管控。
上海
社招6年以上汽车研发
1. 根据整车空调热管理系统的功能与性能需求编制系统功能规范（电器原理图，引脚信号定义、通信信号定义、电气类产品规范参数）； 2. 负责空调热管理系统控制软件功能算法、策略、功能需求定义开发； 3. 负责空调热管理系统软件设计说明文档功能校对； 4. 负责空调热管理系统诊断规范编写； 5. 负责空调热管理系统的各类问题跟踪、分析和解决； 6. 负责空调热管理系统大数据挖掘、分析工作，优化系统功性能表现； 7. 负责空调热管理系统的软件设计开发计划，并推动实施； 8. 负责收集空调热管理个性化、智能化前沿技术追踪、新技术开发； 9. 负责与整车各需求相关部门进行系统需求的沟通协调； 10. 该岗位可base北京/上海。
上海
实习
工作内容： 1.负责整车热管理与能耗预测算法研发、用机器学习算法对整车热管理与能耗进行预测 2.设计并开发时序预测模型； 3.参与完整的数据分析流程：数据清洗、特征工程、模型选型与调优、结果可视化及落地部署； 4.探索前沿时序算法； 5.与业务团队协作，推动模型在真实场景中的价值落地。
更新于 2025-04-29北京