Ridership Analysis & Modeling, Emerging Talent Intern (Fall)

Description

Overview
The Ridership Analysis & Modeling (RAM) department serves as an internal consultant to NYCT on all aspects of ridership patterns, passenger movement, and short- and long-term trends across subway and bus systems. RAM develops and improves models and tools used to estimate statistics and forecast rider behavior during normal operations and in cases of planned work, aiming to improve schedule accuracy and better target customer information.

Responsibilities

• Update and maintain GIS datasets, including the Bus Lane Segments shapefile and Bus Route LION Matching data, ensuring accuracy and consistency across systems.
• Perform spatial data validation, cleaning, and quality assurance to support reliable transit planning and federal reporting outputs.
• Affiliate with integrating GIS datasets with bus speed and operational data to support analysis of corridor performance and running time estimation.
• Develop and refine geospatial analysis tools and workflows to improve efficiency.
• Contribute to the modernization of fixed guideway reporting processes by contributing to updated GIS-based reporting frameworks.
• Affiliate with backend development and data structuring for the OP ArcGIS Online Portal, improving accessibility and usability for internal teams.
• Document data processes, methodologies, and updates to ensure transparency, reproducibility, and long-term maintainability of GIS resources.
• Collaborate with cross-functional teams, including Operations Planning and Data & Analytics, to align datasets and analytical outputs.

Required Qualifications

• Proficiency in Microsoft Office Suite.
• Organizational, analytical, and communication skills.
• Ability to work well under pressure and prioritize tasks effectively.
• A keen eye for detail and ability to work independently while being an active team player.

Required Education

• Matured in a graduate program in good standing with at least 2.8 GPA.
• Major(s) Preferred: City & Regional Planning, Civil Engineering, Transportation Engineering, Urban Studies, Public Policy, Public Affairs, Computer Science, Data Science, or related field.