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The U.S. Department of Transportation is looking for a Fellow to receive training and hands-on experience in data driven disciplines.  This is a great opportunity to strengthen your programming and analysis skills with national data and to gain invaluable experience under the guidance of subject matter experts in supply chain and freight transportation data.  The fellowship offers an excellent opportunity to engage on input, direction, and creativity into projects involving vitally important freight transportation databases as well as skill development and training, plus networking opportunities with government and industry.  If this sounds exciting to you, come apply your data analysis and research skills to help learn how to shape the best U.S. transportation system!

You will be part of the Office of Statistical and Economic Analysis (OSEA). The OSEA develops numerous transportation data products, such as the Freight Analysis Framework (FAF), that are used across the US and internationally to understand transportation trends and inform policy decisions. OSEA staff work collaboratively across agencies, both within and outside USDOT, to explore innovative methods of data collection/analysis/visualization, algorithm development, and modeling methods. Our goals include improving existing data development programs and initiating new ones.

We are currently researching new ways to combine existing data sources to fully leverage available information in data development. You will be involved with this exciting area of data development in one or more projects in freight transportation and supply chain contexts. You will apply data analytics techniques to collect, augment, process, and analyze transportation data. You will be involved with researching primary and auxiliary source data to use in this effort. 

Ready to participate on the intersection of data analytics and transportation? Want to help transform the way data and statistics inform policy decisions? The Bureau of Transportation Statistics (BTS) is the principal federal statistical agency within the U.S. Department of Transportation (DOT), leading the department in data analytics, survey design and data-driven research. We provide independent, objective, comprehensive and relevant information to decision makers and the public; your participation in the positions below will help us examine the extent and use of America’s transportation systems, how those systems perform, and transportation's impacts.

You will have the opportunity to engage national supply chain and freight transportation data programs, participate on research of statistical methodology to enhance data quality and usability, engage innovations to improve efficiency and timeliness of data collection and production, collaborate with BTS privacy and confidentiality efforts. 

Preferred qualifications include: 

• Experience in data processing, programming, quantitative analysis (such as statistics, econometrics, machine learning, or optimization), with knowledge of data mining and predictive modeling. 
• Experience with freight transportation is helpful (but not mandatory).  
• Demonstrated experience in data analytics and quantitative models, with a background that includes one or more methodological areas such as econometrics, statistical modeling, optimization, or data science.  
• Strong communication, coordination, and teamwork skills
• Demonstrated experience with R or Python;
• Demonstrated experience linking and comparing various datasets; 
• Strong written and verbal communications skills;
• Ability to participate under mentor guidance and adapt to changing goals;
• Experience presenting results in a clear and effective manner;
• Some experience with data visualization;
• Ability to participate with others to obtain information, collaborate on data-related projects, and validate findings and conclusions;

• Citizenship: LPR or U.S. Citizen
• Degree: Bachelor's Degree, Master's Degree, or Doctoral Degree.
• Discipline(s):
  • Chemistry and Materials Sciences (12 )
  • Computer, Information, and Data Sciences (17 )
  • Earth and Geosciences (21 )
  • Engineering (27 )
  • Environmental and Marine Sciences (14 )
  • Life Health and Medical Sciences (51 )
  • Mathematics and Statistics (11 )
  • Physics (16 )
  • Science & Engineering-related (2 )
  • Social and Behavioral Sciences (29 )
• Age: Must be 18 years of age