Data & Applied Scientist at Microsoft

Data & Applied Scientist Details

June 20, 2019, 5:12 p.m.
Individual Contributor
Redmond, WA
Capacity Supply Chain
Azure Capacity Supply Chain and Provisioning is looking for a highly motivated data scientist to help us predict, optimize and build the future of cloud computing. Your statistical and machine learning models will drive significant investment and planning decisions for Microsoft's vast and rapidly growing cloud business. You will be faced with problems ambiguous in nature and will need to think creatively and practically to develop effective solutions to address them. You will interact closely with fellow data scientists, PMs, engineers, senior leadership and other stakeholders by acting as a subject matter expert in quantitative methods, and will develop models that chart the course of demand for Microsoft's entire cloud business. CSCP's mission is to deliver capacity for all cloud services through intelligent systems. The organization is responsible for strategy and delivery of the foundational platform for all Microsoft online services including Azure demand and capacity forecasting,

As a data scientist, you will have the responsibility of: Researching and developing production-grade models (forecasting, anomaly detection, optimization, clustering, etc.) for our global cloud business by using statistical and machine learning techniques; Working closely with other data scientists and data engineers to deploy models that drive cloud infrastructure capacity planning; Presenting findings/insights regularly to PMs, stakeholders, and senior leadership; Keeping abreast of new statistical / machine learning techniques and implementing them as appropriate to improve predictive performance
Basic Qualifications: MS or PhD in Computer Science, Statistics, Operations Research or similar applied quantitative field. 3+ years of industry experience in machine learning using R or Python (scikit / numpy / pandas / statsmodel) with skill level at or near fluency. 2+ years experience with deep learning models (e.g., tensorflow, PyTorch, CNTK) and solid knowledge of theory and practice. Preferred Qualifications (in addition to above): 3+ years experience developing production-grade statistical and machine learning code in a team environment. Experience with operations research, optimization
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