阿里巴巴业务技术-数据科学家-ai方向
社招全职1年以上地点:杭州状态:招聘♡ 收藏
工作描述
任职要求 1. 教育背景 计算机、统计学、应用数学、经济学、运筹学或相关专业本科及以上学历(硕士/博士优先)。 2. 基础硬技能 - 熟练掌握 **SQL 与 Python**,具备扎实的概率统计、实验设计与建模功底; - 熟悉主流机器学习方法(GBDT/XGBoost、深度模型、特征工程),并有真实业务建模落地经验; - 熟悉 **因果推断与增量评估** 方法体系(Uplift、DID、PSM、合成控制、HTE); - 对阿里数据研发体系(MaxCompute/ODPS、Hologres、DataWorks 等)有了解或实践经验者更佳。 3. AI / LLM 能力 有数据 Agent、NL2SQL、自动化分析/洞察类产品的设计或落地经验者优先。 4. 业务理解力 具备较强的商业 sense 与结构化思考能力,能把电商场景(行业、人、商品)中的产品与业务问题转化为分析问题,也能把分析结果转化为可执行的产品与业务决策;有 **电商营销、交易、增长、定价/补贴、会员权益** 相关经验者优先。 5. 技术热情与学习能力 有技术信仰,对数据技术前沿(实时流批一体、湖仓、AI Infra 等)保持持续研究,并有热情投入实施与优化。 6. 软性素质 结果导向、皮实抗压…
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包括英文材料
学历+
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.
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.
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.
GBDT+
https://developers.google.com/machine-learning/decision-forests/intro-to-gbdt
Like bagging and boosting, gradient boosting is a methodology applied on top of another machine learning algorithm.
https://scikit-learn.org/stable/modules/ensemble.html
Ensemble methods combine the predictions of several base estimators built with a given learning algorithm in order to improve generalizability / robustness over a single estimator.
XGBoost+
[英文] What is XGBoost?
https://www.ibm.com/think/topics/xgboost
XGBoost (eXtreme Gradient Boosting) is a distributed, open-source machine learning library that uses gradient boosted decision trees, a supervised learning boosting algorithm that makes use of gradient descent.
https://www.youtube.com/watch?v=BJXt-WdeJJo
takes a deep dive into one of the most powerful machine learning algorithm, eXtreme Gradient Boosting, using a Jupyter notebook with Python.
特征工程+
https://www.ibm.com/think/topics/feature-engineering
Feature engineering preprocesses raw data into a machine-readable format. It optimizes ML model performance by transforming and selecting relevant features.
https://www.kaggle.com/learn/feature-engineering
Better features make better models. Discover how to get the most out of your data.
因果推断+
https://web.stanford.edu/~swager/causal_inf_book.pdf
How best to understand and characterize causality is an age-old question in philosophy.
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