Transportation System Data Analyst and Modeler III

Analyze multimodal transportation datasets and develop, validate, and operationalize models to forecast future traffic and reliability across corridors and time horizons. Contribute to transportation data analysis, modeling, and simulation, and develop and implement machine-learning and AI-based algorithms to enhance traffic analysis and forecasting. Collect, clean, and integrate multi-source transportation data to model baseline traffic flows and corridor performance. Develop and apply computer vision techniques for accurate vehicle detection, classification, and counting, using traffic video data to streamline operations. Establish automated methods for data acquisition and processing to streamline vehicle counting processes, improving efficiency in traffic data management. Design machine learning models to forecast traffic patterns and assess the impact of toll versus non-toll roadways, integrating outcomes into four-step travel demand models. Perform advanced transportation modeling using Cube and QGIS on external-external trip distribution. Coordinate technical task management across multiple projects; develop work plans and task lists, assign and schedule team work, track progress to deadlines, and provide technical guidance to ensure on-time, high-quality delivery. Lead big data pipelines for multimodal transportation, performing ETL, QA/QC, and feature engineering to enhance forecasting models. Serve as the technical liaison with clients, support the Project Manager in meetings, communicate plans, schedules, results, and deliverables, and coordinate team engagement with subconsultants, including meeting participation. Oversee project data activities, set and manage timelines, enforce QA/QC, benchmark and review results, and translate findings into clear updates and deliverables for stakeholders. Full-time remote work permissible from anywhere in the U.S. Domestic travel up to 10% of the time.

• A Master’s degree or the foreign equivalent in any Engineering Discipline, Computer Science or related field plus 2 years of experience in a data analysis occupation.
• Two (2) years of experience with research and development of complex data models and algorithms for forecasting purposes
• Two (2) years of experience in Image & Video Processing
• Two (2) years of experience in programming languages such as Python, Java or C

*Any and all experience may be gained concurrently.

Demonstrated knowledge* of:

• ML/AI model development, automation, and data science workflows (preprocessing, feature engineering, and model evaluation).
• Algorithms, data structures, statistical methods, and applied physics/mathematical principles.
• Analysis of large datasets (Big data). Image and video processing techniques, including preprocessing, annotation, and augmentation.
• Python for data analysis (NumPy, Pandas, SciPy) and programming with SQL, MySQL, or MongoDB.
• Data mining and data visualization methods.
• Optimize end-to-end processing time through workload profiling, parallel compute, and incremental data and memory refresh strategies.
• Technical communication for both written and oral presentations

*Knowledge may be demonstrated through coursework, training, and/or experience. Any and all knowledge may be gained concurrently.

OR:

• A PhD or the foreign equivalent in any Engineering discipline, Computer Science or a related field.

Demonstrated knowledge* of:

• ML/AI model development, automation, and data science workflows (preprocessing, feature engineering, model evaluation).
• Analysis of large datasets (Big data). Algorithms, data structures, statistical methods, and applied physics/mathematical principles.
• Image and video processing techniques, including preprocessing, annotation, augmentation, and computational video analysis.
• Python for data analysis (NumPy, Pandas, SciPy) and database technologies (SQL, MySQL, MongoDB).
• Data mining and data visualization methods.
• Optimize end-to-end processing time through workload profiling, parallel compute, and incremental data and memory refresh strategies.
• Technical communication in written and oral formats.

*Knowledge may be demonstrated through coursework, training, and/or experience. Any and all knowledge may be gained concurrently.