字节跳动数据科学（Global Selling）-TikTok Shop

1、深入参与国际化电商的全托管数据科学团队的日常工作，理解业务逻辑并通过数据洞察推动业务优化，包括指标归因监控体系建设，专项策略分析； 2、负责全托管在多个国家的国家经营分析，洞察用户增长、大促、补贴、下单玩法等运营动作可优化点，评估业务ROI并提出优化策略； 3、负责内容场景的转化效率分析，负责短视频\直播场景达人选品&达人任务等业务的洞察分析和策略优化。

PurposeTo bridge business needs and AI capabilities by identifying, designing, and implementing AI-driven solutions that enhance operational efficiency, decision-making, and business innovation. This role acts as a key interface between business functions and IT, ensuring AI initiatives align with organizational goals. Strategic Value • Drive the execution of the company’s AI strategy in China, enabling smarter operations, automation, and data-driven decisions. • Support business growth, cost efficiency, and risk management through AI-enabled solutions with measurable business impact. • Foster an AI innovation culture and establish the foundation for broader organizational transformation. Key Responsibilities 1. Business Analysis – Engage with business stakeholders to understand workflows, challenges, and identify areas where AI can bring measurable improvements. 2. AI Solution Design & Implementation – Work with IT, data teams, and vendors to design and deliver feasible AI solutions aligned with business priorities. 3. Project Management – Manage AI projects end-to-end, ensuring timely and quality delivery with measurable business outcomes. 4. AI Enablement & Adoption – Drive AI adoption initiatives and promote organization-wide awareness and skill development in AI tools and automation. 5. Data & Compliance Alignment – Collaborate with Data Governance, Legal, and Compliance teams to ensure AI initiatives adhere to data protection and local regulations. 6. Performance Evaluation & Continuous Improvement – Measure business impact of AI initiatives and continuously refine solutions based on data-driven insights. Key Success Factors • Strong business acumen to identify valuable AI use cases and drive practical implementation. • Influential communicator who can translate complex AI concepts into actionable business terms. • Continuous learner with curiosity about AI trends, enterprise AI, and data management practices. • Innovative and agile mindset, capable of executing in a fast-evolving and uncertain environment. Key Internal Interfaces • Business Functions (Sales, CS, Manufacturing, HR, etc.) – Co-define AI project goals and business value. • IT & Data Teams – Collaborate on solution design, deployment, and maintenance. • Legal & Compliance – Ensure data protection and regulatory compliance. • Global AI/Digitalization Teams – Align with global strategies and share best practices. Qualifications • Bachelor’s degree or above in Computer Science, Information Management, Data Science, or related fields. • 5+ years of experience in IT, business analysis, or data-driven projects; prior experience in AI or automation initiatives preferred. • Strong understanding of Generative AI, machine learning, and data analytics concepts, with the ability to translate them into business value. • Excellent communication and stakeholder management skills across departments and cultures. • Proficiency in English (both written and spoken) for effective communication and reporting. 岗位目标 通过深入理解业务需求，识别可通过AI技术优化的场景，设计并推动AI解决方案在组织中的落地，提升业务效率、决策质量与创新能力。该岗位将作为业务部门与IT团队之间的桥梁，确保AI战略与业务发展目标保持一致。 岗位价值 • 推动公司在中国区的AI战略落地，帮助业务部门实现智能化、自动化和数据驱动决策。 • 通过AI解决方案支持业务增长、成本优化与风险控制，为公司创造可量化的业务价值。 • 构建跨部门协作的AI创新文化，为未来更广泛的AI转型奠定基础。 主要职责 1. 业务需求分析 – 深入了解各业务部门的流程、痛点与改进空间，识别AI可创造价值的机会。 2. AI解决方案设计与实施 – 与IT、数据团队及外部合作伙伴协作，定义AI解决方案的架构与实施路径。 3. 项目管理与落地 – 负责AI项目从需求定义到交付的全过程管理，确保项目按时、按质完成。 4. AI赋能与推广 – 推动AI工具（如Copilot、自动化、智能分析等）的应用，提升员工AI使用意识和技能。 5. 数据与合规协同 – 与数据治理、法务及合规团队合作，确保AI解决方案符合数据安全和本地监管要求。 6. 绩效评估与持续改进 – 通过数据分析评估AI项目的业务影响，持续优化方案。 关键成功要素 • 能从业务视角发现AI应用场景，并以结果为导向推动落地。 • 在跨部门沟通中具备影响力，能将复杂的AI概念转化为可执行的业务语言。 • 对AI趋势、企业级AI应用、以及数据管理具备持续学习和探索精神。 • 具备创新思维和敏捷执行力，能够在不确定的环境下推动项目向前。

职位：Applied scientist 应用科学家实习生 毕业时间：2025年10月 - 2026年9月之间毕业的应届毕业生 · 入职日期：2025年6月及之前 · 实习时间：保证一周实习4-5天全职实习，至少持续5个月 · 工作地点：北京朝阳区酒仙桥路恒通商务园区 · 校招信息请参考校园招聘申请手册： https://amazonexteu.qualtrics.com/CP/File.php?F=F_55YI0e7rNdeoB6e 投递须知： 1 填写简历申请时，请把必填和非必填项都填写完整。提交简历之后就无法修改了哦！ 2 学校的英文全称请准确填写。 如果您正在攻读计算机视觉、生成式AI或多模态领域的博士或硕士研究生，而且对应用科学家的实习工作感兴趣。 如果您也喜爱深入研究棘手的技术问题并提出解决方案，用成功的产品显著地改善人们的生活。 那么，我们诚挚邀请您加入亚马逊的International Technology自动化营销团队改善亚马逊节假日促销的用户体验。我们的目标是帮助亚马逊的客户找到他们所需的产品，并发现他们感兴趣的新产品。 这会是一份收获满满的工作。您每天的工作都与全球数百万亚马逊客户的体验紧密相关。您将提出和探索LLM和CV领域的创新，例如如何精准控制最前沿的基座大语言模型和图像生成模型以满足自动化的需求。您将集成这些模型到工具链中生成个性化的促销广告图，通过标注数据、建模和客户反馈来完成闭环。您对模型的选择需要能够平衡业务指标和响应时间的需求。

数据收集、回传及处理 1. 搭建完善的广告投放数据监测体系，实时收集、整理与分析广告投放数据，包括曝光量、点击量、转化率、成本等关键指标，深入洞察广告投放效果及用户行为路径； 2. 负责接收、回传、整合来自各大广告平台、公司内部业务系统等多渠道的数据，确保数据的完整性与准确性； 3. 对原始数据进行清洗，识别并处理数据中的缺失值、重复值和异常值，为后续分析提供高质量的数据基础； 4. 协助产研部门建立和维护数据仓库或数据库，优化数据存储结构，提高数据查询和调用的效率。​ 数据分析与挖掘​ 1. 对广告投放数据进行深度分析，挖掘数据中的潜在规律和趋势，为广告投放策略的优化提供数据支持；​ 2. 评估不同广告创意、投放策略的效果差异，为决策提供科学依据； 3. 及时发现投放中的低效，以及舞弊的流量，为公司降低损失。 4. 使用数据可视化工具将复杂的数据转化为直观易懂的图表，清晰展示广告投放效果及关键指标的变化趋势。定期撰写详细的数据报告，对广告投放效果进行全面解读，包括各项指标的完成情况、问题分析以及改进建议。 5. 积极协助产研部门优化投放工具和数据基建，提高效果广告的投放效率。 Data Collection, Transmission, and Processing 1. Establish and improve the ad performance monitoring system to collect, organize, and analyze advertising data in real time, including key metrics such as impressions, clicks, conversion rates, and costs, to gain in-depth insights into campaign effectiveness and user behavior paths. 2. Responsible for receiving, transmitting, and integrating data from multiple sources, including major ad platforms and internal business systems, ensuring data integrity and accuracy. 3. Clean raw data by identifying and handling missing values, duplicates, and anomalies to provide a high-quality data foundation for further analysis. 4. Assist product and engineering teams in building and maintaining data warehouses or databases, optimizing data storage structures to improve query and retrieval efficiency. Data Analysis and Mining 1. Conduct in-depth analysis of advertising data to uncover underlying patterns and trends, providing strong data support for optimizing advertising strategies. 2. Evaluate the performance of different ad creatives and delivery strategies to support data-driven decision-making. 3. Identify inefficiencies and fraudulent traffic in ad campaigns in a timely manner to help reduce company losses. 4. Use data visualization tools to transform complex data into clear and intuitive charts, effectively presenting trends and changes in key ad metrics. Regularly compile detailed data reports to comprehensively interpret advertising performance, including metric achievements, issue analysis, and improvement suggestions. 5. Actively support the product and engineering teams in optimizing ad delivery tools and data infrastructure to enhance the efficiency of performance advertising.