Office 365 continues to lead the cloud transformation for Microsoft and has become the locomotive for services across the Company. With hundreds of thousands of customers and hundreds of million users already on the service, Office 365 is leading the future of services. Customers and Partners are betting on the cloud and expecting Office 365 to deliver an unparalleled experience on the service. Our customers deserve and expect high satisfaction from the service and Office 365 is taking bold steps to deliver to this standard. Office 365 is leading the transformation and redefinition of how we help customers on the service. Our mission is to distinguish O365 support as a cloud-first support model that is optimized for customer experience and based on a culture of satisfaction.
5-7+ years (including MS program or PhD program) of experience working with large data sets or doing large scale quantitative analysis and predictive moldering work. Experience working on various forecast tasks is a big plus.
Bachelor's or Master's degree, or PhD's degree in Computer Science, Mathematics, Physics, Engineering, Statistics or other related technical major fields.
Experience processing large data sets through statistical software (ex. R, SAS) and program language (ex, Python), or other methods. Strong algorithmic problem-solving skills.
Fundamental understanding of statistics, hypothesis testing, p-values, confidence intervals, regression, classification, and optimization is a core requirement.
Experience with one or more of the commercial or open source statistical and various machine learning software.
Experience working with strong SQL skill, Hadoop, Pig/Hive, Spark, MapReduce; Familiarity with Cosmos (via Scope/Spark) is a plus.
Preference towards to the abilities and experiences of conducting forecast, customer churn predicting task etc., and preference also given to the candidates with taking coursework in statistical modeling and inference, machine learning, data mining, time series forecasting.
In terms of business accountability this role is expected to directly contribute to the operation optimization of Office 365 operation and modern support through delivering predictive models and inside discovery, building machine learning driven automated data processes (such as pre-processing, feature extractions) and semi-automated / automated reporting services as well as.
Preferred Qualifications: Experiences of working on various forecast tasks; Expericenes of using Hadoop, Pig/Hive, Spark, MapReduce;(internal Scope); Excellent SQL skill
Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form.
Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.
The Data & Applied Scientists from the Business Intelligence team explore the big data from Office 365 operation and support in search of consistent patterns and systematic relationships (connect the dots) across all data sources involved. The goal of this team is to predict trends, find insides and produce actionable, trustworthy recommendations and decisions for Office 365 operation and modern support, which is curried on through an analytical modelling process based on machine learning by processing structured, semi-structed and unstructured data.
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