夸克智能信息-广告算法工程师-智能营销广告推荐算法
社招全职3年以上技术类-算法地点:北京状态:招聘
任职要求
任职要求 1.学历背景:计算机科学、数学、统计学或相关领域硕士及以上学历。 2.技术能力: 熟练掌握Python/C++,具备扎实的数据分析与建模能力。 熟悉常见机器学习算法(如GBDT、XGBoost、LightGBM)及深度学习框架(如TensorFlow、PyTorch)。 有广告/推荐系统相关经验者优先,了解RTB(实时竞价)、DMP(数据管理平台)等广告技术生态。 3.业务理解: 对广告行业(如DSP、SSP、Ad Network)有基本认知,能结合商业目标设计算法指标。 具备跨部门沟通能力,能将复杂算法逻辑转化为可执行的工程方案。 4.加分项: 发表过顶会论文(如KDD、WWW)或参与过大规模数据竞赛(如Kaggle)。 有大规模分布式计算(如Spark、Flink)经验。
工作职责
岗位职责 1.设计并优化广告推荐策略,包括点击率(CTR)预测、转化率(CVR)建模、ROI建模等; 2.基于各类数据,优化用户画像和场景特征,开发个性化推荐算法,提升广告匹配效率和用户体验,包括但不限于媒体用户冷启优化等; 3.分析广告推荐数据,持续迭代算法模型以提升ROI(投资回报率); 4.与产品、运营团队协作,推动算法在广告平台中的落地实施; 5.研究前沿机器学习/深度学习技术(如大模型、图神经网络),探索广告场景下的创新应用。
包括英文材料
学历+
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.
C+++
https://www.learncpp.com/
LearnCpp.com is a free website devoted to teaching you how to program in modern C++.
https://www.youtube.com/watch?v=ZzaPdXTrSb8
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
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.
算法+
https://roadmap.sh/datastructures-and-algorithms
Step by step guide to learn Data Structures and Algorithms in 2025
https://www.hellointerview.com/learn/code
A visual guide to the most important patterns and approaches for the coding interview.
https://www.w3schools.com/dsa/
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.
LightGBM+
https://lightgbm.readthedocs.io/en/stable/
LightGBM is a gradient boosting framework that uses tree based learning algorithms.
https://www.youtube.com/watch?v=tSZxOd1TWZc
In this video, we explore LightGBM, a machine learning algorithm developed by Microsoft that offers superior speed, efficiency, and accuracy.
深度学习+
https://d2l.ai/
Interactive deep learning book with code, math, and discussions.
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.
推荐系统+
[英文] Recommender Systems
https://www.d2l.ai/chapter_recommender-systems/index.html
Recommender systems are widely employed in industry and are ubiquitous in our daily lives.
Kaggle+
[英文] Kaggle Learn
https://www.kaggle.com/learn
Gain the skills you need to do independent data science projects.
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.
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