Data Scientist 2 at Microsoft

Data Scientist 2 Details

Jan. 16, 2019, 6:31 p.m.
Individual Contributor
Redmond, WA
Azure Storage team Microsoft Azure Storage
The Microsoft Azure Storage team is looking for a Data Scientist/Applied Machine learning engineer with proven capability of using data to solve high impact business problems. You will have the opportunity to use machine learning techniques, statistical analysis to experiment with the data and push the boundaries of how much value we can create for our customers, business and engineering within Azure. You will be able to use the insights you gain from the experiments and show impact towards business by getting to work with our brightest engineers across the Azure stack to deliver promising and impactful end to end results. If you love data, if you want to solve some of the most exciting challenges on a massive product like Azure storage and if you think you have the skills for it, this could be position for you.

Responsibilities and Capabilities Research, define, and develop new methods for analyzing and measuring quality of product features and product releases. Design methodologies to model components of distributed systems. Design and engineer experiments on end to end scenarios. Analyze data and study the system to provide meaningful insights on the product quality. Build predictive models and conduct experiments to gain insights into quality, health of the product and customer usage. Cross-collaborate with engineers on building statistical models, applying machine learning techniques for targeted
Minimum Requirements: BS+ in computer science or related fields 3 + years of experience in developing with C#, C++, of Java 3+ years of work experience with statistical software (R or Python), software programming in an Object Oriented Programming Language e.g. C++, C#, Java Preferred Qualifications: MS/ PhD in fields like computer science, mathematics, statistics, machine learning, operations research, data mining, AI. Proven ability and experience in using data science, statistical computing, and modeling to improve business KPIs. Experience with statistical, predictive modeling, machine learning
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