Microsoft is looking for an energized Data Scientist to join the Microsoft Manufacturing and Strategic Sourcing Digital Transformation team within the Devices organization, responsible for driving manufacturing analytics and BI to the next level. Microsoft Devices is chartered with the design, manufacturing and distribution of all Microsoft hardware including Surface, Xbox, HoloLens and products of the future. The pace of innovation and transformation within Manufacturing and Sourcing is rapid as Microsoft continues the journey to build products and services that help people and businesses throughout the world achieve more. As a data scientist, you will be responsible for building a cutting edge BI platform that rapidly answers the business' questions. This will mean understanding customer requirements, finding available data, integrating available data into the existing BI systems, and exposing the data in an intuitive way to end users though a performant UI. The candidate will understand
Understand the e2e picture of the Manufacturing and Sourcing data and make actionable recommendations on design, and data flow. Own end to end: from an understanding of the source data, to understanding the ETL and aggregations, to understanding the business needs. Bring best BI practices from other parts at Microsoft and beyond across the stack (from aggregating data to UI), including familiarity with a structured approach for onboarding new data. Proven ability to translate business and product questions into analytics projects. Experience working with unstructured and structured data with a
B.S. Computer Science, Industrial Engineering or Supply Chain. 8+ years of building business + technology solutions. Deep understanding of Manufacturing and Sourcing processes, flows and data architectures. Demonstrated ability to influence and drive change across technology and business teams. A MS degree (or a PhD) in a quantitative discipline (Computer Science, Statistics, Mathematics, Physics, Operation Research) is a plus. Background in Machine Learning – Time series, unsupervised classification, decision tree, logistic and multiple regression. Background in Statistics – Parametric, Non-parametric