微软Senior Applied Scientist

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.

• Excellent communication and collaboration skills, with the ability to work across engineering and product management.
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 Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)• OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)
• OR equivalent experience.

• 3+ years' experience conducting research as part of a research program (in academic or industry settings).
• 1+ year(s) experi…

工作职责

• 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.