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Job Details
Posted date: Aug 11, 2025
Category: Research, Applied, & Data Sciences
Location: Redmond, WA
Estimated salary: $158,000
Range: $100,600 - $215,400
Employment type: Full-Time
Travel amount: 25.0%
Work location type: Up to 50% work from home
Role: Individual Contributor
Description
Do you describe yourself as an explorer, builder, or thought leader who thinks differently and has a technical background as an applied scientist, data scientist, quantitative analyst or other analytical discipline? We are seeking trail blazers to join the Substrate Applied Data Science team in M365, where we harness the power of AI to transform how global infrastructure is understood, optimized, and scaled. Our mission is to build intelligent systems that anticipate needs, adapt in real time, and empower Microsoft 365 and Copilot to deliver seamless, resilient experiences to billions of users worldwide.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.
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.
Qualifications
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 fieldOR equivalent experience.1+ years of experience with technical data analysis and modeling experience using Python/R/SQL1+ 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 experienceOR 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 forecastingFamiliarity 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/HiveExperience with analytics/visualization platforms such as Power BI/TableauOther 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.
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form.
Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.
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