微软Senior Applied Scientist--M365
任职要求
Required Qualifications: • Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ 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 3+ years related experience (e.g., statistics, predictive analytics, research) • OR Doctorate 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 equivalent experience. • 4+ years of applied science or data science experience. • Experience in LLM is preferred. • Strong communication skill in both English and Chinese. Other Requirements: Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings: • Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter. Preferred Qualifications: • Master's Degree in Statistics, Econometrics, Computer Science, Electrical• OR Comp…
工作职责
• Drive data exploration and analysis by collecting initial datasets, selecting appropriate analytical techniques, and applying foundational statistical methods to extract insights. • Build and evaluate ML models by running modeling tools on prepared datasets, analyzing performance, and incorporating customer feedback to improve outcomes. • Contribute to AI development by writing production-quality code for features or models, applying debugging best practices, and staying current with industry trends and methodologies. • Champion customer-centric solutions by understanding business goals, identifying growth opportunities, and managing expectations throughout the project lifecycle. • Collaborate cross-functionally with engineering and product teams to define success metrics, improve AI quality at scale, and shape how performance is measured across Copilot technologies.
Lead the design and development of advanced models and algorithms for web search and browsing, collaborate with cross-functional teams, and drive innovation in AI-powered information retrieval.
• Drive data exploration and analysis by collecting initial datasets, selecting appropriate analytical techniques, and applying foundational statistical methods to extract insights. • Build and evaluate ML models by running modeling tools on prepared datasets, analyzing performance, and incorporating customer feedback to improve outcomes. • Contribute to AI development by writing production-quality code for features or models, applying debugging best practices, and staying current with industry trends and methodologies. • Champion customer-centric solutions by understanding business goals, identifying growth opportunities, and managing expectations throughout the project lifecycle. • Collaborate cross-functionally with engineering and product teams to define success metrics, improve AI quality at scale, and shape how performance is measured across Copilot technologies.
• Analyze massive datasets to extract insights and prototype predictive models that forecast infrastructure capacity needs. • Develop scalable solution pipelines to enhance the efficiency, reliability, and performance of Microsoft 365 and Copilot services. • Leverage generative AI and agentic orchestration to build intelligent systems that address complex infrastructure challenges. • Design and implement innovative machine learning and mathematical models to drive breakthrough optimizations. • Collaborate with cross-functional teams—including product, engineering, and research—to align efforts and deliver high-impact solutions. • Translate advanced research into durable, data-driven products that create lasting business value.
• Design and implement offline evaluation strategies that capture real-world usage and reflect end-user preferences. • Develop scientifically sound metrics that diagnose model regressions, benchmark against baselines (e.g., ChatGPT, Glean), and validate product improvements. • Manufacture synthetic yet realistic user activity data using LLMs to simulate diverse usage scenarios. • Collaborate on multi-agent systems or agentic workflows to automate evaluation flows and generate high-signal insights. • Analyze evaluation outputs to identify gaps in coverage, quality, and usability across Copilot canvases. • Partner with engineering and PMs to ensure insights are integrated into product workflows and experimentation pipelines. • Publish learnings in internal forums, external conferences, and contribute to best practices in applied science.