
平安科技算法工程师(数据挖掘方向)
社招全职1年以上计算机网络技术类地点:上海状态:招聘
任职要求
二、技术要求 1.硕士及以上学历,数学、统计学、管理科学、金融工程、计算机科学或相关专业背景。 2.具备 1 年及以上数据挖掘 / 数据科学 / 算法应用相关经验,熟悉 To C/线上 用户数据分析场景。 3.掌握常见的数据分析与建模方法,包括但不限于用户分群、行为分析、生命周期建模、预测模型或推荐模型。 4.熟练使用 Python、SQL 等数据分析与建…
登录查看完整任职要求
微信扫码,1秒登录
工作职责
一、岗位职责 1.面向 To C 线上业务场景,围绕客户全生命周期经营,开展用户/客户数据分析与数据挖掘工作,支持精细化运营、产品增长与商业转化。 2.构建并持续迭代客户画像与分群体系,设计客户动态分群与生命周期演化分析方法,刻画客户结构变化及迁移路径。 3.开展用户行为分析,识别影响用户活跃、留存、转化等关键因素,输出可落地的业务洞察与策略建议。 4.参与推荐、排序、匹配等模型的设计与应用,包括但不限于产品推荐、内容推荐、流量分发与转化预测。 5.与产品、运营、业务及技术团队紧密协作,将业务问题抽象为数据与模型问题,推动数据能力在实际业务中的落地应用。 6.参与数据指标体系与分析方法的建设,提升数据分析与决策支持的系统性和一致性。
包括英文材料
学历+
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
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.
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
NumPy+
https://numpy.org/doc/stable/user/absolute_beginners.html
NumPy (Numerical Python) is an open source Python library that’s widely used in science and engineering.
[英文] NumPy - Learn
https://numpy.org/learn/
Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community.
https://www.kaggle.com/code/themlphdstudent/learn-numpy-numpy-50-exercises-and-solution
This kernel uses exercises of NumPy from the Machine Learning Plus webpage
https://www.youtube.com/watch?v=KHoEbRH46Zk
If you've heard of Pandas and NumPy, you may think one is simply a superset of the other.
https://www.youtube.com/watch?v=QUT1VHiLmmI
Learn the basics of the NumPy library in this tutorial for beginners.
https://www.youtube.com/watch?v=VXU4LSAQDSc
This video serves as an introduction to the NumPy Python library.
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).
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.
还有更多 •••
相关职位
社招3-5年D10702
随本地生活业务多场景AI落地,构建AIGC、B/C端等智能服务的数据飞轮,主要工作涉及: 1. 用户数据分析和策略制定:分析用户交互数据和转化数据,评估不同商品和不同视频内容下用户的转化情况,制定选品、价格和内容优化策略; 2. 优化模型生成效果:负责模型训练数据构建与管理,参与数据筛选、标注及评测体系构建工作。分析和挖掘现有数据资源,通过数据驱动的方法优化,结合A/B测试等手段验证调整效果。
更新于 2025-08-11北京
社招技术类
1. 从事人力招聘领域数据挖掘和运筹优化算法研发和创新落地; 2.包括但不限于,需求预测、定价优化、流量分配、推荐系统、计算广告、自然语言处理等工作; 3.负责上级安排的其他工作。
更新于 2022-02-11北京
社招3年以上技术类-算法
1. 非结构化/多模态数据处理(长文本、短文本、图片、音视频等),结合知识图谱进行实体抽取、归一,属性挖掘,提供在线工程服务。 2. 挖掘用户长/短周期画像,实现多源数据融合,大文本以及多模态表征数据构建以及应用。 3. 结合前沿技术和业务场景,探索创新算法能力。
更新于 2025-09-23杭州|成都