1. Product metric framework: Build core experience metrics that are robust to traffic mix, separating traffic fluctuations driven by channels, paid acquisition, and campaigns from the behavior of genuinely retained users (those who keep creating, return, and pay);
2. User lifecycle: Develop a deep understanding of the end-to-end product experience from input to export, map behavioral data to real task completion and friction points, and identify the high-frequency needs of retained users and the key drop-off points before churn, to inform roadmap prioritization;
3. Apply A/B testing and sample size calculation proficiently, with well-chosen primary and guardrail metrics; identify and control for biases caused by differences in new vs. returning user mix and traffic sources, so that experiment conclusions are trustworthy;
4. Respond quickly to high-priority ad hoc requests; for product metric anomalies, first determine whether the cause lies in traffic/channel mix, data definitions, or the product itself, then deliver root causes and recommendations;
5. Work closely with Product, Design, Algorithm, and Engineering teams; communicate findings, limitations, and recommended actions clearly, drive iterations through to launch, and track outcomes;
6. Collaborate with the data platform and data engineering teams to define tracking specifications, event and metric semantics, and acceptance criteria, and jointly build datasets, self-service analytics tools, and reusable data assets for product experience analysis;
7. Use AI tools effectively to improve the efficiency of data acquisition, analysis, research, and knowledge management.
1. 产品指标体系搭建：建立不受流量结构影响的核心体验指标，区分渠道、投放、活动带来的流量波动，和产品内真实留存用户（持续创作、复访、付费的用户）的行为；
2. 用户生命周期运营：深入了解用户从输入到导出的完整产品体验链路，把行为数据对应到真实的任务完成和体验摩擦点，找出留存用户的高频需求和流失前的关键卡点，为产品路线图排优先级提供依据；
3. 熟练运用 A/B Test 和样本量计算，合理设置主指标和护栏指标；能识别与排除新老用户构成、流量来源不同等带来的问题，确保实验结论可信；
4. 快速响应高优先级的 ad-hoc 需求，对产品指标异常先判断问题出在流量/渠道结构、数据口径还是产品本身，再给出原因和建议；
5. 与产品、设计、算法、研发团队紧密协作，讲清楚分析结论、适用边界和行动建议，推动迭代落地并追踪效果；
6. 与数据中台和数据开发团队协作，定义埋点规范、事件和指标语义、验收标准，一起建设面向产品体验分析的数据集、自助分析工具和可复用的数据资产；
7. 合理使用 AI 工具，提升数据获取、分析、研究和知识沉淀的效率。
任职要求
1. Master's degree or above in Statistics, Applied Mathematics, Economics, Computer Science, Artificial Intelligence, or a related discipline, or an equivalent background;
2. Advanced proficiency in SQL and Python, with hands-on experience across the full analytics workflow, from querying and aggregation to analysis and visualization;
3. Solid foundation in statistics and causal inference: familiar with A/B testing, retention/cohort analysis, and common quasi-experimental methods (DID, PSM, etc.); understands confounding and selection bias, and can tell correlation from causation;
4. Strong product sense and user empathy: experience in user behavior or product analytics, with the ability to understand user motivations and usage scenarios from behavioral data;
5. Strong communication skills and a collaborative mindset, able to explain data findings in language that product and design teams can understand.
1. 硕士及以上学历，统计学、应用数学、经济学、计算机、AI 等相关专业，或有相关专业背景；
2. 精通 SQL 与 Python，有从数据查询、聚合、分析到可视化的全流程实践经验；
3. 统计和因果推断基础扎实：熟悉 A/B Test、留存/Cohort 分析和常用准实验方法（DID、PSM 等），理解混杂因素和选择偏差，能区分相关和因果；
4. 有较强的产品感和用户同理心：有用户行为分析或产品分析经验，能从行为数据理解用户动机和使用场景
5. 沟通能力好、有团队协作精神，能用产品和设计团队听得懂的语言表达数据结论。
