Are you interested in working for one of the most exciting products in Microsoft, passionate about exceeding customer expectations and advancing Microsoft's cloud first strategy? Are you interested in a start-up like environment, excited about cloud computing technology and driving growth in one of Microsoft's core businesses? If so, then look no further than the Azure Customer Experience (CXP) Team!
Microsoft Azure provides customers with an on-demand and infinitely scalable infrastructure and platform for customers to build, host, and scale service applications on the Internet through Microsoft's global data centers. As part of the Azure Engineering organization, Azure CXP is a rapidly growing team committed to driving Azure growth through our relentless pursuit of satisfied Azure customers, by leading world-class customer reliability engagements, engineering modern customer-first experiences for scale, and by driving deep customer insights and empathy into the broader Azure Engineering organization.
We are looking for a strong Data Scientist with a proven track record of solving large, complex data analysis and machine learning problems in a real-world customer focused setting. The ideal candidate should be able to identify a business or engineering problem, translate it to a data science project, determine sources of data, conduct the analysis that will reveal actionable insights, and develop/operationalize models that enable new capabilities and scenarios for our customers and partner teams.
* S. and/or M.S. (Ph.D. Preferred) in Computer Science, Statistics, Operations Research or similar quantitative field
* Solid foundation of machine learning algorithms (classification, regression, clustering, time series forecasting, recommender system, text analytics). Experience with deep learning is a plus.
* Working experience in applying machine learning to one or more of the following fields (forecasting, text analytics, customer CRM, transactional analytics, etc).
* Deep understanding of big data systems, including Spark, Hadoop, Azure Data Lake, Cosmos, Azure SQL, etc.
* Expert in one or more machine learning platforms, such as R, Python, Azure ML
* Expert in one or more scripting languages, such as R, Python, SQL, C++, etc
* Experience building data pipelines to operationalize end-to-end solutions
* 5+ years of applying statistical modeling and machine learning algorithms to real world problems
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include, but are not limited to the following specialized security screenings:
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.
* Work with management and stakeholders, identify opportunities for data science to make an impact, and formulate these opportunities to data science projects.
* Identifies data sources, integrates multiple sources or types of data, and applies expertise within a data source to develop methods to compensate for limitations and extend the applicability of the data
* Applies (and develops if necessary) tools and pipelines to efficiently collect, clean, and prepare massive volumes of data for analysis
* Transforms formulated problems into implementation plans for experiments by applying (and creating when necessary) the appropriate data science methods, algorithms, and tools, and then statistically validating the results against biases and errors
* Interprets results and develops insights into formulated problems within the business/customer context and provides guidance on risks and limitations
* Acquires and uses broad knowledge of innovative methods, algorithms, and tools from within Microsoft and from the larger scientific community, and applies his or her own analysis of scalability and applicability to the formulated problem
* Validates, monitors, and drives continuous improvement to methods, and proposes enhancements to data sources that improve usability and results
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