[Skip to main content](#main-content)

[OpenNash Jobs](https://jobs.opennash.com/)
[Hiring report](/reports/hiring/) · [All Kuaishou Technology jobs](https://jobs.opennash.com/companies/kuaishou-technology/) · [Search jobs](/search/)

Kuaishou Technology · Beijing

# 反诈运营实习生

Beijing 

Listed on Kuaishou Technology’s official careers site. Listed as of October 19, 2026. Check that it is still open and apply there.
[Apply on Kuaishou Technology’s careers site →](https://zhaopin.kuaishou.cn/#/official/social/job-info/31771)

[OpenNash Jobs](https://jobs.opennash.com/) / [Kuaishou Technology](https://jobs.opennash.com/companies/kuaishou-technology/) / 反诈运营实习生

## Job description
- 负责反诈运营日常审计工作，按周/月产出常规分析报告；
- 监控平台内外涉诈风险动态，完成初步分级与响应，协助处理用户客诉与问询；
- 配合完成高风险内容的风险标注与审核辅助工作，整理典型案例并沉淀案例库；
- 协助反诈宣传内容的素材收集、文案整理与基础制作，跟踪活动数据并完成效果统计；
- 支持跨团队协作的日常沟通与信息同步，跟踪待办闭环。
- 本科及以上学历，统计学/理工相关专业优先，对互联网安全/风控领域有兴趣；
- 熟练使用Excel，有SQL或BI工具基础者优先；
- 细心、耐心、执行力强，能处理标准化工作，能够接受周末值班或假期处理风险应急事件；
- 良好的沟通能力和团队协作意识；
- 实习周期不少于3个月，每周至少4天；

### 加分
- 有数据分析、产品运营相关实习经验；对短视频平台生态有一定了解。

## At a glance
- Posted by the employer: Not stated
- First seen by OpenNash: Oct 19, 2026
- Last checked: Oct 19, 2026
- Listed for: Under a day
- Location: Beijing[Apply on Kuaishou Technology’s careers site →](https://zhaopin.kuaishou.cn/#/official/social/job-info/31771)

## More jobs at Kuaishou Technology

### [(大模型专项）多模态大模型算法工程师](https://jobs.opennash.com/companies/kuaishou-technology/jobs/98edf462c05648daadcf/)

Beijing

### [AI Agent 开发实习生（电商在线推荐引擎）](https://jobs.opennash.com/companies/kuaishou-technology/jobs/5945a85b9a833fcbb5f8/)

Beijing

### [AI Agent 策略实习生](https://jobs.opennash.com/companies/kuaishou-technology/jobs/b814e14328b750da25f6/)

Beijing

### [AI Agent算法专家](https://jobs.opennash.com/companies/kuaishou-technology/jobs/0e36be4e740de43e02ad/)

Beijing

### [AI Agent算法专家（智能创作方向）](https://jobs.opennash.com/companies/kuaishou-technology/jobs/41a78c4c892cbbd1bc50/)

Beijing

OpenNash lists jobs from employers’ own careers sites and does not handle applications.
Dates come from the employer where stated; “first seen” and “last checked” are our own observations.

OpenNash Jobs · Official listings with dates and links to the employer. [Weekly hiring report](/reports/hiring/) · [Methodology](/methodology/)