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Columbia Data Science Institute

YES, You Really Can Use This! — Applying Data Science to Real-World Problems

Booz Allen Hamilton’s Dan Liebermann and Ben Arancibia will cover what it takes to get data science done in the real world.  They will be sharing stories from the trenches – covering experiences and lessons learned from turning data science theory into reality when the problem (and the solution) are far from known.  The talk will heavily engage the audience to hear their perspective, and cover the approach Booz Allen took to solve its clients’ problems.  The goal is to get the audience thinking about what they would do in these situations and how they would apply their classroom experience.

Dan Liebermann, a Lead Associate at Booz Allen Hamilton, has a track-record of leading successful analytical and technical engagements while drawing on his project management skills. Dan has ten years of experience conducting data analysis and managing data science projects where he has used his combination of consulting and data science skills to help his clients derive the most value from their data.  His work has included applications in statistics, machine learning, data mining, and population modeling, in addition to supporting the organizational and people sides of developing a data science capability.  Dan has used tools such as R, Bayesialab, and Excel to perform statistical and analytical techniques.  Dan holds a M.P.A. with a specialization in Management from Columbia University’s School of International and Public Affairs, and a B.A. from Carnegie Mellon University.

Ben Arancibia, a Lead Data Scientist in Booz Allen Hamilton’s Strategic Innovation Group has significant experience in statistical analysis, Big Data analytical processes, database development, as well as, the R, and Python programming languages. Ben’s work includes applied data science, building Big Data systems, and web application development. Prior to working for Booz Allen, Ben worked extensively in international development managing software development projects and as a GIS developer. Ben holds a MS in Data Science from the City University of New York where his graduate research focused on fraud detection in financial foreign assistance transactions. He holds an undergraduate degree from William and Mary in quantitative economics.