微软Applied Scientist 2
任职要求
Required qualifications: Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ years related experience (e.g., statistics, predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field OR equivalent experience. • Minimum of 2 years of professional experience in recommender systems, search engines, or online advertising, with strong expertise in machine learning algorithms, generative AI and large language models (LLMs), statistics, and data mining techniques applied to personalization. • Proven problem-solving abilities and a track record of thriving in fast-paced, collaborative environments. Excellent communication and teamwork skills, with the capability to clearly present and explain technical concepts to a variety of audiences. • Proficient in Python programming, with additional experience in languages such as C# or C++ considered a plus. Additional or preferred qualifications: Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ years related experience (e.g…
工作职责
• Bridges the gap between research and development teams to bring state-of-the-art technologies—especially in NLP, LLMs, and recommender systems—into Microsoft products, driving immediate product impact. • Designs, develops, and optimizes ranking and recommendation algorithms (e.g., for News & Feeds), incorporating advanced machine learning and generative AI techniques to improve user engagement and product features. • Performs deep data analysis to uncover patterns and trends, builds systemic models to enhance recommendation relevance and user experience, and prepares high-quality datasets for ML applications. • Applies and improves ML algorithms, prepares and adapts data for scalable AI solutions, and supports deployment, monitoring, and iteration of models in production environments. • Works with internal and external product and business groups, contributes to technology transfer, patents, and white papers, and consults on applying advanced concepts to real-world product needs. • Maintains academic and professional networks, supports recruiting and mentoring efforts, and contributes to publications and internal knowledge sharing to foster innovation and talent development. • Documents research processes and findings, adheres to ethics and privacy standards, and aligns work with team strategy while gaining expertise in key research domains like machine learning, computer vision, and statistical modeling.
LLM Application in Customer Service: Design, develop, and iterate on prompts for various LLM applications within the customer service center, including conversational AI, content generation, and summarization. Experiment with prompt formats, styles, and techniques to optimize LLM performance and output quality. Analyze LLM-generated responses, identify biases or limitations, and implement mitigation strategies to enhance customer satisfaction and service performance. Operational Excellence: Identify and propose innovative projects that leverage LLMs to solve complex problems or explore new capabilities in customer service. Conduct research, experiment with cutting-edge techniques, and develop prototypes for potential products or services. Collaborate with data scientists, machine learning engineers, and other stakeholders to integrate prompts and research findings into the broader AI/ML pipeline. Performance Analysis: Analyze the content of customer interactions to generate self-service rate analysis reports, explaining why customers transfer to agents and what can be done to improve these indicators. Generate regular chat-bot performance reports and continuously improve the algorithm model accuracy, customer satisfaction, and service performance of the chat-bot. Team Collaboration: Work closely with relevant function teams to deliver an integrated workflow of demand negotiations, standard creations, and project implementations. Share knowledge and best practices with the team, mentor junior AI operation peer, and contribute to a collaborative learning environment.
1、基于最新的大模型、深度学习、机器学习、统计学和优化技术，开发创新算法并为业务问题构建原型； 2、通过无监督学习、聚类算法等技术，从海量数据中发现潜在的模式和趋势，提出数据驱动的业务解决方案； 3、与产品经理和跨职能团队合作，定义用户故事和成功指标，管理数据项目从0到1的全过程； 4、使用AB测试等方法验证项目的商业价值和预期收益，并持续优化模型性能； 5、与工程团队合作部署数据模型，并将解决方案规模化。 1.Develop innovative algorithms and build prototypes for business problems using the latest deep learning, machine learning, statistical, and optimization techniques; 2.Use unsupervised learning and clustering algorithms to discover potential patterns and trends from large datasets and propose data-driven business solutions; 3.Collaborate with product managers and cross-functional teams to define user stories and success metrics, managing data projects from 0 to 1; 4.Use methods like AB testing to validate the business value and expected revenue of projects and continuously optimize model performance; 5.Work with engineering teams to deploy data models and scale solutions.
我们正在寻找AI/ML技术的技术专家，包括计算机视觉(CV)，自然语言处理(NLP)和音频信号处理。您将负责与各种利益相关者(产品、运营、政策和工程)合作，并开发最先进的模型。 1、利用自然语言处理、机器学习或计算机视觉等内容理解能力设计和构建产品核心能力，提取数据洞察并优化变现策略； 2、基于最新的深度学习、机器学习、统计和优化技术的算法开发创新性解决方案并构建业务问题原型； 3、从0到1管理数据项目，并与产品经理协作定义用户故事和成功指标来指导开发过程； 4、使用不限于AB测试等方法验证项目的商业价值和预计收益； 5、与工程团队合作部署数据模型并将解决方案规模化。 We are looking for generalists and specialists in AI/ML techniques, including computer vision (CV), natural language processing (NLP), and audio signal processing. You will be responsible for partnering with a variety of stakeholders (product, operations, policy, and engineering) and developing state-of-the-art models. 1. Design and build core capabilities by leveraging content understanding; capabilities, such as natural language processing, machine learning, or computer vision, to extract insights and improve monetization strategies; 2. Develop creative solutions and build prototypes for business problems using algorithms based on the latest deep learning, machine learning, statistics, and optimization techniques; 3. Independently manage data projects from 0 to 1, and collaborate with product managers to define user stories, and success metrics to guide the development process; 4. Verify the business value and estimated revenue of the project using methods such as AB testing; 5. Collaborate with engineering teams to deploy and scale data science solutions.
职位：Applied scientist 应用科学家实习生（博士生） 毕业时间：不限，实习期间需保持在校生身份 · 入职日期：2025年Q3 · 实习时间：保证一周实习4-5天全职实习，至少持续3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NLP，IR或搜索领域专业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搜索团队改善Amazon的产品搜索服务。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NLP和IR领域的创新，基于TB级别的产品和流量数据设计机器学习模型。您将集成这些模型到搜索引擎中为客户提供服务，通过数据，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。