Principal Data Scientist Job at Microsoft Corporation (Redmond)

Job Description

Cloud Operations + Innovation (CO+I) is the team behind one of the World’s largest Cloud Infrastructures, responsible for powering all Microsoft online Products and Services as well as powering Microsoft’s “Cloud First” mission. Our focus is on smart growth, high efficiency, and delivering trusted experience to customers and partners worldwide, and we are looking for passionate, high-energy Principal Data Scientist to help achieve that mission. If you want to have fundamental impact on how Microsoft’s Cloud is deployed and operated globally, be involved with one of the most strategically important teams at Microsoft, and have strong technical and business acumen, leadership, creativity, and out-of-the-box thinking are some of the skills you bring to the table - this job is for you! We are looking for an exceptional Principal Data Scientist to collaborate with our data science and engineering team to build solutions that improve our Data Center delivery cycle time. As a Principal Data Scientist, you will partner with various teams to develop business requirements to standardize the business input and develop a long-term vision and partnering cross-functionally.

Job Responsibility

Lead end-to-end ownership of complex, ambiguous data science initiatives that drive measurable improvements to the global Data Center delivery lifecycle
Architect and deploy scalable statistical, optimization, and machine learning solutions in production to improve forecasting, planning, and operational efficiency
Translate advanced analytical insights into clear, actionable recommendations for senior leadership, influencing strategic and investment decisions
Partner cross-functionally across engineering, operations, supply chain, and finance teams to standardize data definitions and build durable, reusable data products
Establish best practices for experimentation, model validation, monitoring, and governance to ensure reliable and explainable solutions at scale
Mentor and provide technical leadership to other data scientists, raising the quality and impact of analytics across the organization

Requirements

Master's Degree in Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics or related field AND 4+ years experience in data analysis and reporting, data science, business intelligence, or business and financial analysis
OR Bachelor's Degree in Statistics, Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics or related field AND 6+ years experience in data analysis and reporting, data science, business intelligence, or business and financial analysis
OR equivalent experience
Ability to meet Microsoft, customer and/or government security screening requirements
This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter

Nice to have

Master’s Degree in Statistics, Econometrics, Computer Science, Engineering, or related quantitative field AND 8+ years related experience, OR Doctorate AND 5+ years related experience, OR equivalent industry experience
Demonstrated experience leading large-scale, cross-functional analytics initiatives with measurable business impact
Deep expertise in statistical modeling, causal inference, forecasting, optimization, and machine learning techniques
Proven experience designing, deploying, and maintaining production-grade data science solutions in cloud or distributed environments
Advanced proficiency in Python and SQL, with experience building scalable data pipelines and model workflows
Strong executive communication skills with the ability to influence senior stakeholders and operate effectively in highly ambiguous environments
Experience building and operationalizing optimization, simulation, or large-scale forecasting models for infrastructure, supply chain, or complex operational systems
Demonstrated experience influencing data architecture, telemetry design, or platform strategy in partnership with engineering teams
Experience establishing experimentation frameworks, model governance standards, and model monitoring practices across teams

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