Applied Scientist II - M365

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As an Applied Scientist II - M365 on this team, you will be at the forefront of innovation - driving technical vision, guiding strategic decisions, and delivering breakthrough solutions. You’ll harness vast business datasets, state of the art AI models, and deep infrastructure expertise to unlock transformative capabilities that power Microsoft 365 and Copilot, shaping the future of intelligent productivity for hundreds of millions of users worldwide.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

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.
• 1+ years of experience with technical data analysis and modeling experience using Python/R/SQL
• 1+ years of experience with some statistical or machine learning frameworks (e.g., scikit-learn, PyTorch, TensorFlow, MLlib, XGBoost etc.)
  
• 1+ years of experience in a programming language such as Python, R, C# or C++ and a query language, e.g. SQL/HiveQL/Data Analysis Expressions (DAX)/MultiDimensional eXpressions (MDX)

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:

• Doctorate in Statistics, Econometrics, Computer Science, Mathematics, Electrical
  • OR Computer Engineering,
  • OR related field with 1+ year(s) of experience in a quantitative/analytic experience
  • OR Master's Degree in Statistics, Econometrics, Computer Science, Mathematics, Electrical
  • OR Computer Engineering,
  • OR related field AND 3+ year(s) related experience (e.g., statistics, predictive analytics, research)
• Familiarity with time-series forecasting
• Familiarity with leveraging Large Language Models (LLMs) and Agentic AI
  
• Experience with data-engineering basics including Extract, Transform, Load (ETL) pipelines, data-wrangling on a big data platform, e.g. Azure/AWS Data Lake/Hive
• Experience with analytics/visualization platforms such as Power BI/Tableau

Other Requirements:

Applied Sciences IC3 - The typical base pay range for this role across the U.S. is USD $100,600 - $199,000 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $131,400 - $215,400 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay

Microsoft will accept applications for the role until August 25, 2025.

#M365Core #Substrate #Copilot #DataScience #AppliedScience #Foundation

• 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.