Data & Applied Scientist at Microsoft

Data & Applied Scientist Details

March 7, 2019, 1:19 a.m.
Individual Contributor
Redmond, WA
Engineering (PIE) team Artificial Intelligence/Machine Learning
Do you want to be part of Microsoft's journey in making Azure hugely successful? Do you want to build the next generation of cloud services that can manage and operate itself by predicting, detecting and mitigate outages automatically? Do you want to build Artificial Intelligence/Machine Learning solutions to solve real-world technical and business problems? Do you enjoy working with planetary scale data? If these challenges excite you, this is the opportunity for you! Azure Production Infrastructure Engineering (PIE) team is hiring a Principal Data Scientist to help us create an intelligent cloud system which learns from all the Azure operational data and makes Azure operation fully automated with minimal human effort to achieve the highest level of reliability and efficiency. Azure PIE team is responsible for providing all the foundational systems and tools to build, operate, and support entire Azure and many Microsoft Cloud Services. These systems and tools include engineering CI/CD

As a Principal Data Scientist in the Azure PIE team, you will have vast amount of data to analyze and work with. Working with a variety of Azure service teams closely, you will combine their domain knowledge with the data and generate insights to help improve Azure reliability, engineering efficiency, and supportability. Based on the learnings, you will also create algorithms and machine learning models which can make high confidence decisions and recommendations quickly based on data from multiple sources. Once these models are validated and proven, you will work with our engineering teams to
PhD in ML/AI/CS/EE or related areas is required 10+ years of software industry experience, with at least 5 years working with Analytics, AI/ML, AI, NLP, Deep learning, data science or operations research 5+ years research experience on statistical machine learning, deep learning, data mining and optimization Preferred Qualifications Experience in any of the deep learning frameworks (Keras, TensorFlow, CNTK, etc.) required Experience in design of experiments (DoE), applying scientific method to business problems, and hypothesis testing required Ability to synthesize complex issues/scenarios into
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