字节跳动数据分析师（广告产品方向）-Global Business Solutions

1、根据业务发展需求及公司策略导向，搭建业务健康度的经营分析框架和数据基建体系，通过目标的拆解和过程指标体系设定，指引业务方向； 2、定期对负责的工作项目进行专题分析和方案复盘，有针对性的对核心经营指标、异常数据进行分析，输出明确业务改善建议； 3、主动发现业务本质的需求和痛点，找到业务机会，制定合理目标并给出具体可行的建议，推进落地； 4、测算、跟踪、复盘业务目标、重点产品目标、销售团队考核目标。

Team Introduction: TikTok is a global short-video platform available in 150 countries and regions. Our mission is to inspire creativity and bring joy by helping users discover real and interesting moments that make life better. TikTok's global headquarters are in Los Angeles and Singapore, and we also have offices in New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo. TikTok Research & Development (R&D) Team: The TikTok R&D team is dedicated to building and maintaining industry-leading products that drive the success of TikTok’s global business. By joining us, you'll work on core scenarios such as user growth, social features, live streaming, e-commerce consumer side, content creation, and content consumption, helping our products scale rapidly across global markets. You'll also face deep technical challenges in areas like service architecture and infrastructure engineering, ensuring our systems operate with high quality, efficiency, and security. Meanwhile, our team also provides comprehensive technical solutions across diverse business needs, continuously optimizing product metrics and improving user experience. Here, you'll collaborate with leading experts in exploring cutting-edge technologies and pushing the boundaries of what's possible. Every line of your code will serve hundreds of millions of users. Our team is professional and goal-oriented, with an egalitarian and easy-going collaborative environment. Research Project Introduction: As the world's leading short-video platform, TikTok faces multiple challenges in its recommendation systems, including data sparsity for new users leading to insufficient personalisation, high timeliness requirements for live steaming recommendations, difficulty in maintaining user interest diversity, and complex e-commerce recommendation system chains. Traditional recommendation methods heavily rely on historical behaviour modeling, which struggles with the cold-start problem for new users. Live-streaming recommendations demand real-time responsiveness to rapidly changing content dynamics (e.g., host interactions, traffic fluctuations) within extremely short time windows (typically within 30 minutes) posing higher demands on the system's real-time perception and decision-making capabilities. Additionally, the immersive single-feed format amplifies the challenge of maintaining content diversity, requiring a careful balance between multi-interest learning and the risk of content drift caused by exploratory recommendations. The current e-commerce recommendation system follows a multi-stage funnel architecture (recall–ranking–re-ranking), which often leads to inconsistent chains, high maintenance costs, and an overreliance on short-term value prediction. This leads users to fall into content homogenization fatigue. To address these pain points, this project proposes leveraging large language models (LLMs) and large model technologies to achieve significant breakthroughs. On one hand, LLMs—with their vast knowledge base and few-shot reasoning capabilities—can infer new users' potential intentions from registration data and external knowledge, thereby alleviating cold-start issues. On the other hand, by integrating graph neural networks (GNNs) and full-lifecycle user behavior sequences for modeling social preferences, we aim to improve the accuracy of interest prediction. Additionally, the project explores the generalization capabilities, long-context awareness, and end-to-end modeling strengths of large models to simplify the e-commerce recommendation chains, enhance adaptability to real-time changes, and improve exploratory recommendation effectiveness. The ultimate goal is to build a more streamlined system with more accurate recommendations, enhancing user experience and retention while driving sustainable business growth. 团队介绍 ： TikTok是一个覆盖150个国家和地区的国际短视频平台，我们希望通过TikTok发现真实、有趣的瞬间，让生活更美好。TikTok 在全球各地设有办公室，全球总部位于洛杉矶和新加坡，办公地点还包括纽约、伦敦、都柏林、巴黎、柏林、迪拜、雅加达、首尔和东京等多个城市。 TikTok研发团队，旨在实现TikTok业务的研发工作，搭建及维护业界领先的产品。加入我们，你能接触到包括用户增长、社交、直播、电商C端、内容创造、内容消费等核心业务场景，支持产品在全球赛道上高速发展；也能接触到包括服务架构、基础技术等方向上的技术挑战，保障业务持续高质量、高效率、且安全地为用户服务；同时还能为不同业务场景提供全面的技术解决方案，优化各项产品指标及用户体验。 在这里， 有大牛带队与大家一同不断探索前沿， 突破想象空间。 在这里，你的每一行代码都将服务亿万用户。在这里，团队专业且纯粹，合作氛围平等且轻松。 课题介绍： TikTok作为全球领先的短视频平台，面临新用户数据稀疏导致的个性化推荐不足、直播推荐时效性要求高、用户兴趣多样性维护困难以及电商推荐系统链路复杂等多重挑战。传统推荐方法依赖历史行为建模，难以解决新用户冷启动问题，且直播推荐需在极短窗口期内（通常30分钟内）实时捕捉内容动态变化（如主播互动、流量波动），这对系统的实时感知与快速决策能力提出更高要求。此外，单列沉浸式场景放大了多样性问题，需平衡多峰兴趣学习与探索引发的内容穿越风险。当前电商推荐系统采用多阶段漏斗架构（召回-排序-混排），存在链路不一致、维护成本高、过度依赖短期价值预测等问题，导致用户易陷入内容同质化疲劳。 针对上述痛点，项目提出结合大语言模型（LLM）和大模型技术实现突破：一方面利用LLM的海量知识储备与Few-shot推理能力，通过注册信息与外部知识推理新用户潜在意图，缓解冷启动问题；另一方面，在社交偏好建模中融合GNN与用户全生命周期行为序列，提升兴趣预测精准度。同时，探索大模型的泛化能力、长上下文感知及端到端建模优势，简化电商推荐链路，增强实时动态适应性与兴趣探索能力，最终实现系统更简洁、推荐更精准、用户体验与留存双提升的目标，推动业务可持续增长。

1.负责广告业务数据的预测、监控与分析，建立异常预警机制，对异常数据快速定位并推动问题解决； 2.洞察客户需求、行业趋势与市场竞争变化，挖掘广告业务增长机会，输出可落地的策略建议； 3.基于数据分析结论，协同业务、技术、运营团队，推动广告产品优化、策略迭代与效果提升； 4.构建业务关键指标体系，设计并优化数据分析模型，提升数据驱动决策的效率与准确性； 5.定期输出分析报告，为业务决策、资源调配与长期规划提供数据支持。

关于团队 作为目前全球领先的短视频平台，我们的使命是鼓励创作，激发创意。依托于短视频平台的高速增长，国际化商业广告产品，希望连接起全球广告商和客户，建立一个易于理解、易于使用的强大的广告平台。 国际化商业产品数据科学团队旨在以数据和科学的方法，引领产品决策迭代，实现收入的可持续增长。我们诚邀优秀的数据科学家一起，建立面向全球的接触团队，打造一流的广告平台。 广告策略团队聚焦于广告定向、竞价机制、投放策略和各类投放产品的相关数据科学工作。团队致力于通过数理统计、模型技术、科学衡量等方式，最大化广告投放的效果&效率，维护广告交易环节的安全和真实性，极致用户和广告的匹配精准度，平衡用户和平台长短期利益，打造业内领先的营销策略内核。作为广告策略团队的数据科学家，我们和产品经理、研发以及其他跨职能团队紧密合作，提供数据洞察，推动和支持广告系统的创新和成长。 主要职责 1、负责商业化流量、广告产品、投放策略等方向的业务指标体系搭建，对短期异常进行归因，对长期趋势进行解读； 2、深入理解广告投放机制，通过探索性的数据分析与数据建模，从数据中挖掘流量、产品、策略方面存在的问题，并提出相应的优化方案； 3、主动挖掘业务问题，通过对数据的敏锐洞察、定性和定量分析、模型建设、以及实验验证，完成较为深入的专项数据分析，给出决策建议，推动策略应用落地； 4、与产品经理、算法研发、数仓研发以及其它跨职能团队完成高质量沟通，保证推动数据分析结论的落地与持续优化。