Senior Data Scientist

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This team plays a crucial role in supporting Microsoft 365 and the Experiences + Devices group (E+D), both of which are central to the Company's mission to empower individuals and organizations to achieve more. Our objective is to foster a data-informed culture by equipping the Experiences + Devices group with actionable insights to guide their decisions. This initiative represents a significant opportunity to deliver impactful information that will improve operational efficiency, drive empowerment, and support Microsoft’s success in the evolving AI landscape.

The IDEAS Data Science team (Business Pillar) specializes in transforming complex data into actionable business insights. Our focus is on building robust analytical frameworks and predictive models that drive strategic decisions across the organization by integrating various cross-signals. By leveraging advanced data engineering, statistical analysis, and machine learning, we deliver accurate business metrics, optimize performance, and uncover growth opportunities. Our mission is to empower stakeholders with data-driven intelligence that fuels innovation and operational excellence.

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:

• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
  • OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
  • OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.
• 3+ years of experience with SQL, R, Python to implement statistical models, machine learning, and analysis (Recommenders, Prediction, Classification, Clustering, etc.) in big data environment.
• 2+ years of experience with written and verbal communication to educate and work with cross functional teams.
• 1+ year of experience in delivering on ambiguous projects with incomplete or imperfect data.

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:

• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
  • OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
  • OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 7+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.
• 3+ years of experience with SQL, R, Python to implement statistical models, machine learning, and analysis (Recommenders, Prediction, Classification, Clustering, etc.) in big data environment.
• Experience on large scale computing systems like COSMOS, Hadoop, MapReduce and/or similar systems.
• Experience with programming skills, e.g. Java, C#.
• Familiarity with deep learning toolkits, e.g. CNTK, TensorFlow.

Data Science IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 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 $158,400 - $258,000 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 October 24, 2025.

• Design and execute controlled experiments to validate hypotheses.
• Apply advanced statistical techniques (e.g., regression, Bayesian methods, causal inference) to derive actionable insights.
• Communicate limitations and confidence intervals clearly to stakeholders.
• Develop predictive and prescriptive models using machine learning and AI techniques.
• Ensure models are production-ready, maintainable, and aligned with privacy and compliance standards.
• Monitor model performance and iterate based on telemetry and feedback.
• Build ad hoc and production ready data pipelines for large-scale, diverse datasets.
• Partner with Data Engineering teams to ensure robust, secure, and scalable data infrastructure.
• Implement best practices for data quality, governance, and monitoring.
• Translate complex analytical findings into clear business recommendations.
• Influence product and strategy decisions through data-driven insights.
• Collaborate with cross-functional teams to align analytics with organizational goals.
• Coach junior data scientists on technical skills and best practices.
• Drive adoption of efficient processes and tools across teams.
• Contribute to defining standards for experimentation and analytics.
• Adopt new AI/ LLM technologies.
• Embody our culture and values.