Matt currently is a global lead instructor for General Assembly's Data Science Immersive program in ten cities across the U.S and most enjoys bridging the gap between theoretical statistics and real-world insights. Matt is a recovering politico, having worked as a data scientist for a political consulting firm through the 2016 election. Prior to his work in politics, he earned his Master’s degree in statistics from The Ohio State University. Matt is passionate about putting the revolutionary power of machine learning into the hands of as many people as possible. When he isn’t teaching, he’s thinking about how to be a better teacher, falling asleep to Netflix, and/or cuddling with his pug.

A couple months ago Andres Meneses and I sat down at a busy Adams Morgan cafe in the heart of Washington, DC to discuss his success as a frontend developer. Incredible to his story is how he navigated from a successful long-term position at The World Bank to a coding boot camp to gain a foothold in the web development world. His background is one of the many success stories popping up from mid-career individuals making a jump into the world of data science/technology development.

One of the world's top data science boot camps, General Assembly, offers world-class instruction from leading data science instructors. In this three-part series, we interview the staff who train, enable and get nascent data scientists employed.

Ben Miller is an Associate Economist at the RAND Corporation and a Professor at the Pardee RAND Graduate School. His research spans a wide variety of topics including disaster mitigation, infrastructure finance, energy resilience, geospatial information, insurance, tax policy, regulatory affairs, healthcare supply, agriculture, econometrics, and beyond.  Recent publications examine the link between flood insurance prices and housing affordability, review federal policies surrounding transportation and water infrastructure finance, estimate the causal impact of weather warning systems on fatalities and injuries from tornadoes, and overview econometric techniques for determining the value of geospatial information. Prior to joining RAND, Miller worked as a statistician supporting the U.S. Census Bureau’s Survey of Income and Program Participation. He holds a Ph.D. in economics from the University of California, San Diego and a B.S. in economics from Purdue University.

Dr. Paul J. Nicholas is an operations research scientist at the Johns Hopkins University Applied Physics Laboratory (JHU APL). He also teaches a graduate course on analytics and decision analysis as part of GMU’s Data Analytics Engineering (DAEN) program and serves in the U.S. Marine Corps Reserve. Paul’s research focuses on the use of advanced analytic techniques to examine problems in national security, including large-scale combinatorial optimization, simulation, and data analytics. Paul received his B.S. from the U.S. Naval Academy, his M.S. from the Naval Postgraduate School, and his Ph.D. from George Mason University. On active duty, he deployed several times to Iraq and Afghanistan in support of combat operations as a communications officer and operations research analyst. He was a visiting fellow to the Johns Hopkins University Applied Physics Lab, and an MIT Seminar XXI Fellow. He is the author of numerous peer-reviewed publications and holds two U.S. patents.

Josh Lipsmeyer is a Software Engineer/Data Scientist at Radiant Solutions where he specializes in algorithm development, data science, and application deployment primarily for customers with the Department of Defense. His primary interest is the study of complex network resilience, information diffusion, and evolution.  He has a background in mathematics and physics, holding an MS in mathematics from the University of Tennessee. He currently lives in Centreville, VA with his wife and 1-year-old son.

Nicholas Kallfa is a data scientist at Booz Allen Hamilton where he works on creating tailored products for government clients using R and mathematical computing techniques to visualize information in R’s open-source platform Shiny. Not only is Nicholas a great communicator of advanced mathematics, but he shared with us the various methods he uses to guide clients through difficult concepts to reach a mutual understanding. This interview was quite enjoyable since there was an abundance of cats knocking things over in the background. If there is residual noise, I blame it on them.

Anton is a data scientist working in an international development organization in Washington, D.C., where he applies his programming and research skills to contribute to data-for-good goals. While Anton is a full-stack data scientist, meaning he works through a project from prototyping to production, he has a keen affection for natural language processing and text analytics. Prior to starting his data science journey, Anton worked in tech companies and Silicon Valley startups

Patrick Smith is the data science lead at Excella in Arlington, Virginia, where he developed the data science practice. Previously, he both led and helped create the data science program at General Assembly in Washington, DC, as was a data scientist with Booz Allen Hamilton’s Strategic Innovations Group. Prior to data science, Patrick worked in risk and quantitative securities analysis, and institutional portfolio management. He has his B.A. in Economics from The George Washington University and has done masters work at Harvard and Stanford Universities in AI and computer science.

Patrick is passionate about AI and deep learning and has contributed to significant research and development projects as part of Excella’s artificial intelligence research effort. He architected Excella’s DALE intelligent assistant solution which provides state of the art results in question answering tasks and is the author of an upcoming book on Artificial Intelligence.