College Aide

Job Description

Develop a systematic data quality control, standardization, and visualization toolkit to support NYC DOT’s transition toward a more strategic, data-driven approach to citywide mobility analysis. Working directly with the Data Systems & Counts Management team, the College Aide will build new and modify existing workflows to ingest, clean, and validate multimodal traffic counts and third-party location-based mobility data. Where data permits, the project will focus on identifying spatial errors, automating repetitive data-cleaning pipelines, and mapping anomalies across citywide networks. Findings and data pipelines will directly shape how NYC DOT validates major data streams, tracks program performance metrics, and supports larger regional planning models to ensure transportation investments promote equitable and efficient mobility. Typical duties required: Typical duties include reviewing current Python-based data workflows and counts management platform metrics for familiarity. Then, the College Aide will assist in drafting data documentation standards and scripting automated quality control checks using Python and SQL. Additionally, manipulating large spatial datasets and cross-referencing field counts with third-party mobility matrices will be a substantial part of the project. Finally, the College Aide will translate complex data errors into actionable insights, compiling findings into maps, dashboard components, and technical presentations for the engineering and planning teams. What will the student learn from this experience? The student will gain highly marketable, hands-on experience in public-sector data science and transportation planning under the direct mentorship of data professionals. They will deeply develop their technical toolkit in Python, SQL, and GIS by tackling real-world, large-scale mobility datasets. This opportunity provides an inside look at metropolitan planning data infrastructure, allowing the student to build a portfolio piece that bridges technical data engineering with impactful urban policy that supports agency leadership.

Minimum Qualification Requirements

For Assignment Level I: Matriculation at an accredited college or graduate school. Employment is conditioned upon continuance as a student in a college or graduate school. For Assignment Level II (Information Technology): Matriculation at an accredited college or graduate school. Employment is conditioned upon continuance as a student in a college or graduate school with a specific course of study in information technology, computer science, management information systems, data processing, or closely related field, including or supplemented by 9 semester credits in an acceptable course of study. For Assignment Level III (Information Technology Fellow): Matriculation at an accredited college or graduate school. Employment is conditioned upon continuance as a student in a college or graduate school with a specific course of study in information technology, computer science, management information systems, data processing, or other area relevant to the information technology project(s) assigned, including or supplemented by 9 semester credits in an acceptable course of study. Appointments to this Assignment Level will be made by the Technology Steering Committee through the Department of Information Technology and Telecommunications. SPECIAL NOTE Maximum tenure for all Assignment Levels in the title of College Aide is 6 years. No student shall be employed more than half-time in any week in which classes in which the student is enrolled are in session. Students may be employed full-time during their vacation periods.

Preferred Skills

Strong problem-solving, data management, and independent research skills. Advanced familiarity or strong willingness to learn data visualization using Python and SQL. Familiarity and willingness to learn Geographic Information Systems (GIS) software for spatial analysis and mapping. Ability to create clear tables, charts, graphs, and presentation materials. Excellent organizational skills and a strong interest in multimodal data collection, transportation planning, and citywide mobility analysis.