A complete application package consists of:
All documents must be in English or include an official English translation.
If you have questions, send an email to USDOT@orau.org. Please include the reference code for this opportunity in your email.
Are you ready to bring transformative change to Federal information production and distribution techniques? Here is a chance to influence and improve the effectiveness of data at the national level by simplifying complex problems with data and visuals that convey information in new, understandable ways. Take an appointment with our team as we lead the creation of new national transportation data information products in the era of big data, and where you build new stories about transportation and implement innovative analysis through data journalism.
The future of transportation datasets holds great promise as well as new challenges. The democratization of data and omnipresence of technology in transportation has created the opportunity for vast new empirical data resources with profound impacts on data velocity, volume and bias. These changes push us, as storytellers, journalists, analysts and statisticians, to rethink our relationship with data. In this 24 hour a day instantaneous information environment, the pressure to deliver fresh data stories is relentless. A constant call to balance the decennial, quinquennial or even annual data releases with near real time data and analysis exists for many data providers. Working in coordination with our Director of Public Affairs and our lead Visual Information Specialist, you will use data journalism to explore opportunities for advancing data and statistical information and you will build new data stories and information products for Bureau of Transportation Statistics (BTS) to publish to the world.
You will be using, adapting and transforming data and data analysis to provide new stories about transportation. You will craft aviation, economic, freight, passenger, vehicle, and ship data into meaningful information for the public. You will learn to design and build innovative, new stories that capture emerging areas of transportation.
You will have the opportunity to develop close working relationships with other U.S. Department of Transportation (DOT) offices, and other Federal agencies that maintain data and analytical capabilities, which can aid in the understanding of the transportation industry. You will participate in inter-agency efforts relating to the development and improvement of information production and distribution, and you will conduct research on existing and emerging technologies, processes and approaches that can be used to enhance the storytelling of transportation and then implement this research with new products and techniques.
Who are we? We are the U.S. Department of Transportation's Bureau of Transportation Statistics (BTS) Office of Spatial Analysis and Visualization (OSAV). The BTS is the Principal Federal Statistical Agency that provides objective, comprehensive, and relevant information on the extent and use of the Nation’s transportation system, how well the system performs, and the effects of the system on society and the environment. BTS is recognized as the pre-eminent source of airline data as well as freight data for the United States.
Professional Development: $2,500
Relocation : $2,000
Health Insurance stipend: $3,000
This program, administered by ORAU through its contract with the U.S. Department of Energy to manage the Oak Ridge Institute for Science and Education, was established through an interagency agreement between DOE and USDOT. The appointment is full-time at USDOT in the Washington DC, area. Participants do not become employees of USDOT, DOE or the program administrator, and there are no employment-related benefits.
Applicants must have received bachelor's degree in a related field within the last five years. Preferred disciplines include Data Science, Data Visualization, Geography, Social or Behavioral Science, Computer Science, Information Systems, Mathematics, Statistics, Earth or Geoscience
The ideal candidate will have a combination of the following: