Microsoft Advertising's mission: To deliver the most relevant knowledge to our customers by being more than just a search engine - Bing's goal is to be the "decision engine." Data is critical to achieving that mission. At Bing, we have an enormous wealth of data, ranging from user interaction logs to web documents, from user feedback to system performance data. The Bing Advertiser Sciences Team is hiring talented, highly motivated and productive individuals with expertise in the areas of: Computer Science, Machine Learning, Econometrics, Statistics, Modeling, Simulation and Data Mining. The team develops and applies advanced techniques to turn our petabytes of data into insights; and to drive actions based on those insights. The team works closely with partners across Microsoft's Online Services Division to enable rigorous, effective, and data-driven decision making. Some example of the challenges we face: Modeling the dynamics of the paid search market Understanding Advertiser value, lifecycle,
Develop and manage and develop analyses and algorithms that generate actionable insights and programs to improve Bing Ads demand generation activities including: increasing both long-term revenue and relevance. Research and develop solutions for improving profits for Microsoft and returning value to the audience, advertisers and publishers (e.g. Ecosystem health, marketplace performance measurement, advertiser health, outlier detection, etc.). Specific responsibilities include the following: Work with key business stakeholders to understand the underlying business needs and formulate, communicate
A Master's degree or equivalent experience in Data Science, Computer Science, Electrical Engineering, Machine Learning/AI or related fields. Demonstrated experience in all phases of managing data science engagements including: problem definition, solution formulation and delivering measurable impact. Experience with online data; experience with online-advertising data strongly preferred. Knowledge and experience in at least three of the following areas: machine learning, data mining, user modeling, information retrieval (interrogation of log files and very large databases), economic modeling, econometrics,