Junior Transportation Safety Data Scientist

Vanasse Hangen Brustlin Inc.; Charlotte (NC); On-site; USD 85,000 - 95,000; Full time

Job summary

Vanasse Hangen Brustlin Inc. is seeking a Data Scientist (Transportation Safety) to join our Metro DC or North Carolina offices.

You will work on data-driven transportation safety and planning projects for state DOTs, federal agencies, and local governments, handling large datasets, statistical analysis, and visualizations. You will collaborate with a team of transportation planners and traffic safety engineers, applying regression, clustering, and forecasting methods, while developing reusable

Qualifications

• 0-2 years of experience with advanced analytics in either an academic or professional setting.
• Master's degree in Transportation Engineering, Data Science, Statistics, Mathematics, or equivalent discipline; or equivalent experience.

Responsibilities

• Mine and analyze structured and unstructured data to identify patterns, trends, and risk factors.
• Apply statistical and machine-learning methods to transportation safety and planning problems.
• Evaluate model fit and uncertainty, document methods and results for reproducibility.
• Extract data from databases, APIs, spreadsheets, and other sources; clean and join data.

Skills

Statistical modeling

Machine learning

Python

R

SQL

Data visualization

Communication skills

Education

Master's degree in Transportation Engineering

Master's degree in Data Science

Master's degree in Statistics

Equivalent experience

Tools

Tableau

Power BI

Excel

Python

SQL

R

Job description

Vanasse Hangen Brustlin Inc. is seeking a Data Scientist (Transportation Safety) to join our Metro DC or North Carolina offices.

You will work on data-driven transportation safety and planning projects for state DOTs, federal agencies, and local governments, handling large datasets, statistical analysis, and visualizations. You will collaborate with a team of transportation planners and traffic safety engineers, applying regression, clustering, and forecasting methods, while developing reusable