Jeremy Mc Dowell
About Jeremy Mc Dowell
Jeremy McDowell is a hydrologist with extensive experience in geosciences, currently working at the U.S. Geological Survey since 2019. He has held various positions in the field, including internships and roles in hydraulic fracturing and GIS.
Work at U.S. Geological Survey
Jeremy McDowell has been employed at the U.S. Geological Survey (USGS) as a Hydrologist since 2019. His role involves conducting research and analysis related to hydrology. Prior to his current position, he served as a Hydrologist Student Trainee at USGS from 2016 to 2019, gaining valuable experience in the field. His work is based in Fort Worth, Texas, where he has contributed to various hydrological studies and projects.
Previous Experience in Geoscience
Before his tenure at USGS, Jeremy McDowell worked in several roles related to geoscience. He was a Geoscience Intern at Pioneer Natural Resources Company for three months in 2018, located in Irving, Texas. Additionally, he served as a GIS Specialist at Atmos Energy for five months in 2016 in Dallas, Texas. His earlier experience includes working as a Hydraulic Fracturing Field Engineer and Well Cement Laboratory Technician at Advanced Stimulation Technologies, Inc. from 2014 to 2015 and 2013 to 2014, respectively, in Midland, Texas.
Education and Expertise
Jeremy McDowell holds a Bachelor of Arts in Geography from The University of Texas at Austin, which he completed from 2007 to 2012. He furthered his education by obtaining a Master of Science in Geosciences from The University of Texas at Dallas, graduating in 2019. His academic background provides a strong foundation for his work in hydrology and geoscience, where he employs various geospatial analysis tools and numerical modeling techniques.
Technical Skills and Research Focus
In his current role, Jeremy McDowell utilizes advanced technical skills in geospatial analysis, employing tools such as QGIS, ArcGIS, and Google Earth Engine. He specializes in numerical modeling tools, including MODFLOW, SWB, and PEST, to analyze hydrological data. His research also incorporates machine learning techniques to handle large-scale geospatial data and develop climate projections using numerical models. Additionally, he conducts geophysical studies that involve both borehole and surface techniques.