
唯品会高级/资深算法工程师(流量机制侧)
社招全职3-5年地点:广州 | 上海状态:招聘
任职要求
1. 计算机相关专业,本科及以上学历,2年以上搜推广算法经验 ,参与过召回、排序、机制、NLP、CV算法工作(满足一项即可)。优秀的问题解决能力、主动性及跨团队协作能力; 2. 在机器学习、数据挖掘、推荐系统、运筹优化等至少一个领域有扎实理论基础和研发经验; 3. 扎实编程能力,熟练使用Java…
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工作职责
工作内容 1. 支持唯品会电商日常好货好价商品培育,新品成长,大促活动流量分发工作; 2. 对商家、商品、品牌、用户行为做深入理解分析,优化货品、流量策略,制定针对性算法优化流量效率。 3. 利用大规模机器学习算法,深化召回、CTR/CVR模型优化,在线控制算法等,引入强化学习、多目标学习、迁移学习 等前沿技术,解决多任务多目标约束下的流量效率最大化问题。 4. 构建全域流量协同策略,整合搜索、推荐、跨场景流量, 设计全域流量算法框架 ,实现多场景用户触达与转化效率最大化; 5. 理解电商业务知识,参与业务规则制定和流量策略优化,通过流量机制算法策略达成公司层面战略目标。
包括英文材料
学历+
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/
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.
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.
数据挖掘+
https://www.youtube.com/watch?v=-bSkREem8dM
Database vs Data Warehouse vs Data Lake
https://www.youtube.com/watch?v=7rs0i-9nOjo
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.
还有更多 •••