Data Scientist, Lifetime Value Practice, Customer & Market Research at Microsoft

Data Scientist, Lifetime Value Practice, Customer & Market Research Details

Feb. 20, 2019, 12:40 a.m.
Engineering
Individual Contributor
Full-Time
Redmond, WA
market research team Data Science professional
How would you like to bring your data science skills to join one of the largest market research teams in the world? We are seeking an experienced Data Science professional with strong communication, consultation and advanced analytics skills to join our Microsoft Lifetime Value Practice . At Microsoft, we are committed to the mission of empowering our customers to 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. 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

Driving marketing decisions through the generation of cross product insights from the Microsoft Standard Metrics (MSM) data set, using SQL and other tools Creating new models to help predict customer behaviors and actions Consulting with various teams to help better understand their business problems and how the lifetime value dataset can help Empowering others to use Lifetime Value data through the creation of easy to use data views and tools
Required: Minimum of Bachelors degree in Statistics, Economics or Computer Science or any quantitative field 3+ years of professional experience in data science, research or analytics roles High proficiency with SQL required for data access/integration Experience with statistical modeling, machine learning algorithms and experimental design applied to real world problems Ability to apply (or develop, if necessary) tools and pipelines to efficiently collect, clean, and prepare massive volumes of data for analysis Experience creating scalable and replicable solutions for analysis and modeling Excellent
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