亚马逊Applied Scientist Intern, International Technology, 2025 Beijing

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。

A successful candidate will have deep experience in applied methods (statistical analysis, predictive modeling, classification, impact measurement), experience building reporting dashboards, and using those methods and tools to drive key business insights. These insights include but not limited to market growth opportunities and strategies, product opportunities, and advertiser behaviors. The role requires data science skills (i.e., a broad knowledge of existing data mining algorithms), strong business acumen, and knowledge on mobile ads market. You’ll work closely with a broad range of data professionals as well as business leaders. You’ll be working on projects where tools and methods are used to drive real-world business value. - Support DI, Product, Marketing, Partner Development with analytics for customer insights and product opportunity areas. - Quantify the impact of product, sales, and marketing initiatives on customer happiness, and future behavior. - Manage business analytic projects through all phases, including data quality, data modeling, algorithm/KPI development, statistical analysis, data visualization, and presentation of results and deliverables. - Monitor usage metrics, understanding business-based explanations for large scale trends, and patterns in customer lifecycle behavior.

Bringing the State of the Art to Products  Collaborates with and bridges the gap between researchers (in community, Microsoft Research [MSR], or in their own organizations) and development teams. Brings new technology and approaches into production by applying long-term research efforts to solve immediate product needs.  With limited guidance from others, works to create product impact. Identifies approach, and applies, improves, or creates a research-backed solution (e.g., novel, data driven, scalable, extendable) to positively impact a Microsoft product or service. Solves components or aspects of a problem as assigned by a senior team member. May publish research to promote receiving new intellectual property for product impact.  Participates in collaborative relationships with relevant product and business groups inside or outside of Microsoft and provides expertise or technology to create business impact. Participates in technology transfer attempts, filing patents, authoring white papers, developing or maintaining tools/services for internal Microsoft use, or consulting for product or business groups. May publish research to promote receiving new intellectual property for business impact.  Capability Management and Networking  Maintains ties with external network of peers and identifies prospective talent, when asked. May contribute to publications on research findings. May participate in candidate interviews. Collaborates with the academic community to develop the recruiting pipeline and establish awareness of their work.  Reinforces a positive environment by applying best practices. May support mentorship by assisting with onboarding of research interns or other entry-level team members, if applicable.  Documentation  Performs documentation of work in progress, experimentation results, plans, etc. Documents scientific work to ensure process is captured. Participates in the creation of informal documentation and may share findings to promote innovation within group.  Ethics and Privacy  Understands and follows ethics and privacy policies when executing research processes and/or collecting data/information.  Leveraging Applied Research  Applies strategy by understanding the role in the team and applying the strategy provided by senior team members and incorporates state-of-the-art research. Asks probing questions to better understand strategy.  Researches and develops an understanding of tools, technologies, and methods being used in the community that can be utilized to improve product quality, performance, or efficiency. Contributes knowledge around several specialized tools/methods to support the application of business impact or serves as an expert in a deeply specialized area.  Gains deep knowledge in a service, platform, or domain and acquires knowledge of changes in industry trends and advances in applied technologies. Consults with engineers and product teams to apply advanced concepts to product needs. Learns product domain by reviewing products.  Machine Learning Functionality, Insights, and Technical Tools  Prepares data to be used for analysis by reviewing criteria that reflect quality and technical constraints. Reviews data and suggests data to be included and excluded. Describes actions taken to address data quality problems. Assists with the development of useable datasets for modeling purposes. Supports the scaling of feature ideation and data preparation. Helps take cleaned data and adapts for machine learning purposes, under the direction of a senior team member. Seeks guidance from senior team members when confronted with problems/challenges.  Uses machine learning algorithms that structures, analyzes, and uses data in product and platforms to train algorithms for scalable artificial intelligence solutions before deploying. Begins to develop new machine learning improvements independently while under the direction of a senior team member.  Collaborates to leverage data to identify pockets of opportunity to apply state-of-the-art algorithms to improve a solution to a business problem. Uses statistical analysis tools for evaluating Machine Learning models and validating assumptions about the data while also reviewing consistency against other sources. Begins to independently run basic descriptive, diagnostic, predictive, and prescriptive statistics. Assists with the communication of insights under the direction of senior team members.  Supports the application and use of intelligence created during the training of algorithms for deployment. Seeks information about large-scale computing frameworks, data analysis systems, and modeling environments to improve models. Helps create a model, apply the model to real products, and then verify effects through iterations. Helps with experiments by putting multiple models in production and evaluating their performance. Sets up monitoring and implementation to track production models, under the direction of a senior team member. Addresses models when that break, under the direction of others.  Leverages or designs and uses machine learning/data extraction, transformation, and loading (ETL) of pipelines (e.g., data collection, cleaning) based on data prepared. 

• Build benchmarks, evaluation datasets, metrics, and methods to assess and improve the performance and effectiveness of language models and prompts and drive iterative enhancements. • Deliver high-impact analyses that generate actionable insights to steer product and business decisions and improve user satisfaction. • Collaborate closely with engineering, research, product, and other teams to ensure AI-driven experiences meet quality and user experience standards. • Analyze latest AI innovations and explore opportunities to apply cutting-edge techniques for building scalable, high-impact solutions that enhance product capabilities and deliver exceptional user experiences.