Entry Level Transportation Safety Analyst

Key Responsibilities

• Mine and analyze structured and unstructured data, including crash records, roadway inventories, demographic data, surveys, and geospatial datasets, to identify patterns, trends, and risk factors.
• Apply statistical and machine-learning methods, including regression, classification, clustering, and forecasting, to transportation safety and planning problems.
• Evaluate model fit and predictive performance and assess uncertainty, sensitivity, potential bias, and limitations using appropriate validation techniques. Document methods, assumptions, and results to support review and reproducibility.
• Identify and recommend new applications for existing data sources and opportunities to collect or integrate additional data.
• Extract data from databases, application programming interfaces, spreadsheets, text files, and other systems. Clean, link, transform, and quality-check data from multiple sources.
• Use Python, R, SQL, and related tools to automate data preparation, analysis, model execution, validation, and reporting. Develop reusable code and maintain clear technical documentation.
• Apply transportation safety methods, including crash prediction, Safety Performance Function development, before-and-after evaluation, systemic safety screening, risk scoring, and prioritization.
• Prepare and distribute analytical findings. Communicate methods, results, and practical implications to project teams, clients, management, and other technical and nontechnical audiences through effective visualizations, reports, and presentations.
• Work closely with the manager and more experienced data scientists to receive direction and guidance throughout projects, including guidance on analytical methods, missing data, model selection, and interpretation of results.
• Participate in professional development activities aligned with career goals and project needs.
• Perform all job functions in compliance with applicable federal, state, and local regulations and VHB policies and procedures.

Requirements & Tech Stack

• Fundamental knowledge of statistical modeling, quantitative methods, machine learning, data mining, simulation, and advanced mathematics.
• Experience applying quantitative methods to real-world problems through academic research, internships, or professional work.
• Experience using many of the following technologies: Python (Pandas, NumPy, SciPy), R (tidyverse), SQL, Tableau, Power BI, advanced Microsoft Excel, machine learning, statistical analysis.
• Ability to identify information needs, conduct research, and evaluate the quality and suitability of data sources.
• Excellent intrinsic problem-solving, analytical, and mathematical skills.
• Ability to work effectively in ambiguous and technically challenging environments.
• Proficient with Microsoft Office Suite.
• Excellent verbal, written, and interpersonal communication skills.
• Team-oriented with the ability to perform multiple tasks independently in a timely manner and collaborate effectively and positively with coworkers.
• Self-motivated, highly organized, and detail-oriented.

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.