Applied Machine Learning Engineer Ii
Redmond, Washington, Us
Azure Security Engineering Team- Research of exploited vulnerabilities and ongoing attacks as well as methods to protect and detect future attacks.- Perform BigData analytics to check for attack indicators, identify normal vs. abnormal system/ user behavior and detect anomalies.- Develop a security analytics toolset, automation, and application of machine learning models which decreases Mean-Time to Detection (MTTD) and Mean-Time to Recovery (MTTR) for fraud and compromises.- Work with other Azure teams to act on data insights and to improve detection or defensive strategies.AWARDS/RECOGNITIONS- Winner of CloudML(AzureML) Tech Transfer Challenge 2014: Synthetic Minority Over-Sampling Technique (SMOTE) module- 12th place in AzureML March Madness 2015 competition- 5th place out of 40 in AzureML March Madness 2016 competition PRESENTATIONS- M. Olszewski and B. Smith. "Be Careful of the Company You Keep." presented at Practice of Machine Learning Conference Fall 2014, Microsoft, Redmond, WA.- P. Arewar, M. Olszewski, and B. Smith. "Finding Web Attacks in IIS Logs At Cloud Scale" presented at Blue Hat Briefing Day 2015 and Machine Learning and Data Science Conference Spring 2015, Microsoft, Redmond, WA.- B. Smith. "Lessons Learned While Developing a Web Attack Detector for Azure Security Center" presented at Machine Learning, Analytics, and Data Science Conference Spring 2016, Microsoft, Redmond, WA.