Data & Applied Scientist at Microsoft

Data & Applied Scientist Details

Jan. 3, 2019, 7:52 p.m.
Individual Contributor
Redmond, WA
Market Research team 2012 Best Places
At Microsoft, we are committed to the mission of helping our customers realize their full potential. We are motivated and inspired every day by how our customers use our software, services and devices to find creative solutions to business problems, develop breakthrough ideas, and stay connected to what's most important to them. As winners of the 2012 Best Places to Work, we are equally committed to ensuring our employees have the working environment and the resources to succeed and develop as professionals in their disciplines. The Customer and Market Research team is one of the largest market research departments in the world and serves the company's core businesses. We are based at our HQ in Redmond, WA. We are close to and in constant contact with our business partners and our work has global reach and impact. Our people are research professionals with a strong consultative approach who thrive on tackling significant business opportunities and influencing decision makers at all levels

We are seeking an experienced data science professional who would support Microsoft's Lifetime Value practice. The role will influence decisions across marketing, finance and engineering. Candidates will need to use both descriptive statistics and advanced analytics to help us understand behaviors and patterns within data. This includes, but is not limited to, designing predictive customer models that enable the business to drive marketing changes and beyond. This is a transformative agenda and role that is critical to our organization and Microsoft as a whole. We're looking for a candidate with
Bachelors/MS in Statistics, Economics or Computer Science 3+ years of experience in data sciences, research or business roles High proficiency with SQL required, experience with R, SAS or Python highly preferred Previous experiences dealing with large data sets is a must; data architecture, management and storage experience preferred Applies (or develops if necessary) tools and pipelines to efficiently collect, clean, and prepare massive volumes of data for analysis. Experience with statistical modeling, machine learning algorithms and experimental design applied to real world problems Transforms
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