阿里巴巴LAZADA-E-commerce Traffic&Campaign Operations Specialist-Hangzhou

【平台介绍】 Miravia，隶属于阿里巴巴集团，是一家新兴的欧洲电商平台。自2022年底推出以来，Miravia迅速成长为西班牙增长最快的电商平台之一。该平台涵盖超过1000万种商品，尤其在美容（Beauty）和快速消费品（FMCG）领域表现突出。Miravia凭借阿里巴巴的强大背书和先进技术，在西班牙乃至欧洲市场占据重要地位，其用户基数和商品种类持续增长，目前已经成为西班牙乃至欧洲地区重要的电商平台之一。 【Miravia Introduction】 Miravia, affiliated to Alibaba Group, is an emerging European e-commerce platform. Since its launch at the end of 2022, Miravia has rapidly grown into one of the fastest growing e-commerce platforms in Spain. The platform covers more than 10 million products, especially in the fields of beauty and FMCG. With the strong endorsement and advanced technology of Alibaba, Miravia occupies an important position in the Spanish and even European markets. Its user base and product categories continue to grow. It has now become one of the important e-commerce platforms in Spain and even Europe. 【职位描述】 负责Miravia搜索产品的建设，包括分析用户搜索的使用场景和效果，通过下钻到人群、场景、类目、国家等维度以挖掘用户痛点与需求，升级迭代搜索产品（包括召回排序，交互样式，产品卖点等），以提升搜索的相关性和效率 1、负责Miravia搜索产品的整体规划和设计，提升用户搜索体验和效果，推动业务增长。 2、分析用户的使用场景和行为数据，挖掘用户需求，迭代搜索产品，优化搜索算法，提高搜索的转化率。 3、与技术团队紧密合作，推动搜索技术的创新和落地，确保系统的稳定性和响应速度。 4、关注行业动态，研究竞争对手，制定并实施搜索产品的差异化策略。 【Job Description】 Responsible for the construction of Miravia search products, including analyzing the user scenarios and effects of user searches, exploring user pain points and needs by drilling down to crowds, scenarios, categories, countries and other dimensions, and upgrading iterative search products (including recall sorting, interactive styles, product selling points, etc.) to improve the relevance and efficiency of search 1. Responsible for the overall planning and design of Miravia search products, improving user search experience and effects, and promoting business growth. 2. Analyze user usage scenarios and behavior data, explore user needs, iterate search products, optimize search algorithms, and improve search conversion rates. 3. Work closely with the technical team to promote the innovation and implementation of search technology (especially AI), and ensure the stability and response speed of the system. 4. Pay attention to industry trends, study competitors, and formulate and implement differentiation strategies for search products.

Assist in monitoring website traffic and preparing basic data reports. Support content planning and publishing for brand campaigns and product launches. Collect user feedback and coordinate with teams on website experience improvements. Contribute to user engagement activities and basic user segmentation tasks. Provide operational support for membership benefits and campaigns.

Team Introduction Our E-commerce is a content-driven commerce business built on globally-oriented short video platforms. Our mission is to become the go-to platform for users to discover and access high-quality products at great prices. Through multiple scenarios such as livestream e-commerce and video e-commerce, we aim to deliver a more personalized, proactive, and efficient shopping experience for users, while offering merchants a reliable platform to grow their business. We are committed to making great-value products easy to sell and easy to find across more regions, bringing a better life within reach for everyone. We invite you to grow with us, explore, innovate, and unlock your full potential as we tackle both technical and business challenges together. Our team brings rich experience in international product development, embraces diverse cultures, and operates R&D teams across the globe. Join us in facing the exciting challenges of cross-border collaboration, with opportunities for business travel and international assignments waiting for you! 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 ： 国际电商是以国际化短视频产品为载体的内容电商业务，致力于成为用户发现并获取优价好物的首选平台，在直播电商、视频内容电商等多场景下，国际电商业务希望能为用户提供更个性化、更主动、更高效的消费体验，为商家提供稳定可靠的平台服务，在更多的地区实现没有难卖的优价好物，让美好生活触手可得的使命。我们邀请你来此成长、钻研，发掘无限的潜力，一起应对技术和业务上的挑战。目前团队拥有丰富的国际化产品研发经验，包容多元的文化，且在全球设立研发团队，邀请你来一起接受跨国合作的挑战，还有出差外派机会在等你！ 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。

•Monitoring performance data, capable to realize growth opportunity and drop risk • Coordinating cooperation of commercial, operation and many other internal teams to drive key strategy/project/action launch • Communicating and building traffic SOP of campaigns and promotional events on the platform to the key account management team • Collecting key blocker of Key Account Sellers and initiate solution to maintain sellers' investment • Liaising with the key account management team to develop seller segmentation targets and build performance plans specific to each target • To be fully responsible for the implementation of strategies as agreed with stakeholders