微软Senior Applied Scientist (M365 Copilot team)

• 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.

• 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.

• 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.

• Ship features with PM & Engineering. Co‑own scenario goals; translate product requirements into scientific plans and productionized solutions that meet quality/latency/cost targets. • Model development & optimization. Design, fine‑tune, and evaluate models for LLM‑based authoring, summarization, reasoning, voice/chat, and personalization (e.g., SFT, alignment, prompt/tool use, safety filtering, multilingual & multimodal). • Data & evaluation at scale. Build/extend data pipelines for curation/labeling/feature stores; author offline eval harnesses; run online A/Bs and interleavings; define guardrails and success metrics; author scorecards and decision memos. • Production ML engineering. contribute to service code and configs; add monitoring, tracing, dashboards, and auto‑scaling; participate in on‑call and postmortems to improve live‑site reliability. • Responsible AI. Produce review artifacts, document mitigations for safety/privacy/fairness, support red‑teaming and sensitive‑use checks, and align with Microsoft’s Responsible AI Standard. • Collaboration & mentoring. Partner across PM/ENG/Design/CE/ORA/CELA; share methods and code, review PRs, improve reproducibility and documentation; mentor junior scientists.