Channel and Intellectual Property Protection Services Investigative Data Scientist Come as you are. Do what you love. Microsoft a global company, located in over 100 countries, with revenues of over $80bn. Many people think Microsoft = software, but we also do hardware, services, research and more. Think Windows, Windows Server, Azure and Visual Studio. Online services such as Bing and MSN. Business solutions like Office and Exchange. And devices like Surface, Xbox, and more. We're passionate about what we do. We run our company in much the same way. Our employees come as they are and do what they love. We work hard, but we value work/life balance, and each of us defines what that means to us. So why not explore what we do, where we do it, and what life is really like at Microsoft? You just might be surprised. If you love the pursuit of excellence and are inspired by the challenges that come through driving innovations that impact how the world lives, works and plays, then we invite you
Responsibilities: You will be responsible for analyzing data from multiple sources within Microsoft. You will use that data to identify trends, exploits, and scope of non-genuine activity. You will contribute to pattern recognition algorithms, both operational and ad-hoc basis. You will use that information to work across virtual teams to raise awareness of the issue, scope, and remediation phases. You will regularly track activity and report out activity and progress related to various cases. You will also contribute to help evaluate long term controls which will help improve systems and tools
Basic Qualifications: A Bachelors degree in Computer Science, Criminal Justice, Mathematics, Statistics, Data Science or a related field, or equivalent alternative education, skills, and/or practical experience is required 3 years' experience minimum in Criminal Investigation, Data Science, Data Analysis, or Business Intelligence Preferred Qualifications: Experience analyzing big data and modelling data Formulates problems to inform multiple dimensions of business/customer questions using algorithms and multiple data sources. Uses data-exploration techniques to discover new or previously unasked