About the job
As a Principal Data Scientist – AI Infrastructure Data team member, you will own end-to-end delivery of strategic data science projects and partner with customers and internal teams to design and implement advanced analytics and AI solutions that create measurable business impact. This hands on role blends deep technical expertise with consulting and stakeholder engagement, enabling you to influence decisions and guide adoption of data-driven strategies. In this role, you will focus on data science for AI infrastructure: turning fleet-scale telemetry, experimentation, and partner requirements into decisions that improve reliability, efficiency, capacity planning, and customer outcomes across Microsoft’s AI systems.
Responsibilities
Own delivery of complex, high-impact data science and AI solutions for strategic consulting engagements.
Collaborate with stakeholders to define business problems and translate them into actionable AI-driven solutions.
Acquire, clean, and prepare large datasets for modeling.
Build and deploy predictive and prescriptive models using modern machine learning techniques.
Write efficient, maintainable code and ensure scalability for production environments.
Present findings to senior stakeholders using compelling storytelling and visualizations.
Simplify complex ML/AI concepts for diverse audiences to drive understanding and adoption.
Act as a trusted advisor to internal teams and customers, ensuring solutions meet business needs.
Promote responsible AI practices, including fairness, transparency, and explainability in model and application development.
Stay current with emerging AI technologies, frameworks, and tools to continuously enhance solution capabilities.
Qualifications
Minimum
Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ 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 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
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
Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 4+ 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 7+ 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 9+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
OR equivalent experience.
Proven consulting and stakeholder engagement skills with proven ability to influence decisions.
Proficiency in Python and SQL; experience with cloud platforms (Azure preferred).
Knowledge of Responsible AI principles and ethical data practices.
Experience with broader software engineering lifecycle practices, including version control, testing, DevOps, and production deployment of Machine Learning (ML) solutions.
Experience with AI-assisted coding practices and specification-driven development.