Senior Applied Data Scientist – Microsoft Advertising  

Gain expertise in a building identity graphs including applicable research techniques. Build deep knowledge of a service, platform, or domain, and identify product needs by sharing the latest industry trends and applied technologies. Review business requirements and incorporate research to meet business goals. Provide strategic direction for the kinds of data used to solve problems and apply deep subject matter knowledge to support business impact. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) These requirements include but are not limited to the following specialized security screenings: Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. 3+ years experience conducting research as part of a research program (in academic or industry settings). 1+ year(s) experience developing and deploying live production systems, as part of a product team. 1+ year(s) experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping. Experience with developing graph based identity models to perform cross device user stitching, applying deterministic and probabilistic matching algorithms to construct large-scale identity graphs.