Interested in getting a better sense of what it’s like to work as a statistician at a National Lab? What kind of research do these institutions get involved in? What sorts of opportunities exist?
If you are interested in the answers to these questions - and more about National Labs - then this webinar is for you!
More importantly, how do you prepare yourself for obtaining a position?
Once again, NISS will be gathering senior statisticians who bring extensive experience working in these specialized federal laboratories. In addition, these individuals have graciously made it a priority to share their experiences so that others may better understand and be better prepared for working in a national lab.
Each presenter will have 15 minutes to address the following general topics:
- What are the job opportunities for statisticians/data scientists/analysts in your National Lab?
- Describe the range of skills statisticians/data scientists/analysts need to succeed in your National Lab?
- What is the career path for statisticians/data scientists/analysts in your lab?
- Is your agency currently hiring statisticians/data scientists/analysts?
- What advice would you give to students based on your experience?
This is not a session to be missed!
Nancy McMillan, (Battelle)
Emily Michele Casleton, (Los Alamos)
Gabriel Huerta, (Sandia)
Seiyon (Ben) Lee, (Department of Statistics, George Mason University)
The Career Fair will be conducted using Zoom.
Open and Free to Anyone Interested
About the Speakers
Nancy McMillan leads the Advanced Analytics Division at Battelle, with responsibility for a portfolio of Government and commercial contract research programs in data applications, statistics, and machine learning. Her team provides machine learning and artificial intelligence expertise to cross-disciplinary research projects across Battelle. Dr. McMillan is a Data Scientist with over 20 years experience working in a research environment. Her technical expertise is focused on providing quantitative analysis that captures uncertainty to support science-based decision-making, particularly for problems that require analysis of big data. Project Management Professional since 2011.
Emily Michele Casleton was recruited to the Statistical Sciences group (CCS-6) at Los Alamos National Laboratory as a summer student at the 2012 Conference on Data Analysis. She joined the Lab as a post doc in 2014 after earning her PhD in Statistics from Iowa State University. Since converting to staff in 2015, Emily has routinely collaborated with seismologists, nuclear engineers, physicists, geologists, chemists, and computer scientists on a wide variety of cool data-driven projects. Most recently, she has been the PI of a data analytics project under the Multi-Infomatics for Nuclear Operations Scenarios venture; co-organizer of the invited CCS-6 seminar series; co-chair of the Conference on Data Analysis, the conference that brought her to LANL a decade ago; and is currently serving as the deputy group leader of CCS-6.
Gabriel Huerta received his Ph.D. and M.S in Statistics from Duke University. Previously, he was a full-time faculty member at the Department of Mathematics and Statistics at the University of New Mexico. He joined Sandia in 2018 as a distinguished member of the technical staff and was recently appointed as National Laboratory Professor with the University of New Mexico .He is an Associate Editor for the journals Bayesian Analysis, Journal of Uncertainty Quantification and Environmetrics. Gabriel Huerta’s research interests include Bayesian methods, time series, space and space/time models, extreme value analysis, and UQ/statistical learning methods. His major projects at Sandia include Bayesian model calibration in material sciences applications, UQ for combined EM/mechanical codes, connections between UQ and machine learning methods and Bayesian approaches for reliability assessment.
Seiyon (Ben) Lee is an Assistant Professor in the Department of Statistics at George Mason University. Before joining GMU, he completed his PhD in Statistics from the Pennsylvania State University. His research interests include computational methods for modeling high-dimensional spatial-temporal data, statistical methods for calibrating complex computer models, and interdisciplinary research in the environmental sciences. He is particularly interested in developing scalable methods to model various types of massive spatio-temporal data sets and statistical methods for studying climate change. At GMU, he is also part of the collaboration core between GMU and the INOVA Health System.