Note: The job is a remote job and is open to candidates in USA. The Oak Ridge Institute for Science and Education is offering a fellowship opportunity with the Bureau of Transportation Statistics (BTS), a federal statistical agency within the U.S. Department of Transportation. The role involves participating in data analytics and research projects focused on transportation systems, utilizing network analysis techniques to enhance data quality and inform policy decisions.
Responsibilities
- Participate in the Office of Statistical and Economic Analysis (OSEA) where you will join a team of highly skilled data scientists, engineers, and other research fellows
- Develop numerous transportation data products, including the Freight Analysis Framework (FAF), which is used across the US and internationally to understand transportation trends and inform policy decisions
- Collaborate across agencies, both within and outside DOT, to explore innovative methods of data collection/analysis/visualization, algorithm development, and modeling methods
- Research new ways to apply network flow algorithms at a large scale to simulate nationwide U.S. freight flows
- Learn and apply network analytics techniques to evaluate the flow of cargo throughout the Nation’s complex multimodal system, which includes highway, rail, water, pipeline, air, and multimodal networks
- Research primary and auxiliary source data during this appointment and gain experience in applying network analysis methods in a high-powered, cloud-based framework, and in crafting analytical tools that inform policy
- Learn and influence national data programs, perform research on statistical methodology to enhance data quality and usability, research innovations to improve efficiency and timeliness of data collection and production, learn BTS privacy and confidentiality efforts, and learn how to represent BTS in various settings such as conferences
Skills
- Be at least 18 years of age or older
- Be a U.S. Citizen or Lawful Permanent Resident
- A bachelor's, master's, or doctoral degree in a relevant field is required
- The degree must have been conferred within 60 months of the appointment start date
- Demonstrated experience in network assignment methods and network flow simulation as they relate to transportation
- Background that includes one or more quantitative areas such as transportation engineering, network science, machine learning, statistical modeling, optimization, or data science
- Experience with R or Python
- Some experience with other languages such as C or C++
- Demonstrated experience linking and comparing various datasets
- Strong written and verbal communications skills
- Ability to participate with limited supervision and to adapt to changing goals
- Experience presenting results in a clear and effective manner
- Some experience with data visualization
- Proficiency in translating high level business requirements into research-based tasks and results
- Experience or interest in applying modeling and simulation methods to applications involving multimodal freight, shipping, and logistics related topics
- Ability to liaise with others to obtain information, collaborate on data-related research projects, and validate findings and conclusions
Benefits
- A travel allowance
- Health insurance
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