Never before have information, analytics, and artificial intelligence been so important to business success. That momentum is matched with Microsoft's industry leadership and investment in empowering business transformation. The core business objectives of the ASD organization are to improve IT health outcomes for Microsoft's largest business and enterprise customers, and to enable profitability of the Microsoft Services business via packaged, highly-scalable digital delivery assets. We do this through three primary deliverables: A platform for performing customer IT health assessments, a catalog of IP that includes digital learning assets and workshops to help educate customers, so they can make the most of their IT environments, and a portal called the Services Hub that ties it all together. We have over 20,000 active customers and are working to improve our platform and the services we deliver on top of it and driving the future of the entire Services business. What kind of team is ASD?
ASD develops a myriad of self-service options for Microsoft customers on the Services Hub platform that help digitally transform their business. We run assessments that auto-discover enhancements to their on-premise or cloud footprints and provide recommendations for the adoption of best practices. AI and machine learning play a key role in this process. You will work towards understanding this landscape and provide expert guidance on the usage of data science to help drive the business value for Services Hub. The term 'lead' applies to four basic elements of the role. These are relentless customer-driven
Required Qualifications: 2+ years of experience as a Data Scientist, either working with a team of engineers developing commercially viable SaaS based solutions or a consulting team providing data science solutions to customers. 1+ years of hands-on experience with at least one of the ML related technologies (SAS, SPSS, RevR, Azure ML, MapR). The job assumes a foundation of core data science skills enriched with curiosity, delivery experience in commercial environments, great customer and communication skills, and the experience to lead and mentor less experienced data scientists. More specifically: