Transportation Data Scientist; Jr level
Listed on 2026-01-10
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IT/Tech
AI Engineer, Data Analyst, Data Scientist, Machine Learning/ ML Engineer
Overview
Leidos is seeking a talented Junior Transportation Data Scientist at the junior level to support FHWA-funded projects at the intersection of AI, data science, and transportation. This role involves assisting in the development and deployment of AI/ML models for applications such as vehicle load classification using weigh-in-motion data and imagery, crash prediction in traffic management centers, and creating data ecosystems for trustworthy AI.
The ideal candidate will have foundational experience in AI model development, data integration, and stakeholder engagement, with a passion for applying AI in state-level transportation initiatives in a federally supported research environment.
Location: This role requires full-time on-site work at the customer site in McLean, VA.
Learn about STOL here: STOL information is available on the Highway Research Center’s page.
Primary Responsibilities- Assist in conducting data and literature reviews for AI methods, datasets, and technologies relevant to freight analytics, traffic safety, and operations (e.g., sensor fusion, computer vision, multimodal AI).
- Prepare and integrate datasets for AI use cases, including cleaning, normalizing, enriching, and fusing multi-source data while addressing quality issues like inconsistency, sparsity, and bias (e.g., traffic logs, imagery, weather, permitting records).
- Contribute to the design, development, and deployment of AI/ML models for transportation applications, including classifying oversized/overweight vehicles using WIM data and imagery, crash prediction in TMCs, and generating synthetic data for model training.
- Evaluate AI model performance under diverse conditions and provide recommendations for improving robustness, scalability, and trustworthiness in real-world transportation environments.
- Support stakeholder outreach and engagement, including organizing peer exchanges, workshops, and technical briefings with state DOTs, MPOs, enforcement agencies, and vendors.
- Assist in identifying and pursuing new opportunities with state DOTs for AI initiatives, including roadmaps, proposals, and implementation strategies for AI in ITS.
- Collaborate with cross-functional teams to align projects with FHWA goals, including risk management, quality assurance, and compliance with federal standards.
- Contribute to monthly progress reporting and iterative model refinement based on federal feedback.
- Bachelor’s degree in computer science, Data Science, Artificial Intelligence, Electrical Engineering, Transportation Engineering, or a related field;
Master’s preferred. - Minimum of one (1) year of professional experience in the transportation space, data science, and AI/ML, with familiarity in machine learning frameworks (e.g., Tensor Flow, PyTorch, Scikit-learn), data processing tools (e.g., Pandas, Num Py), and AI techniques (e.g., deep learning, generative AI like GANs, computer vision).
- Experience with transportation-specific data sources (e.g., HSIS, SHRP2, NGSIM) and standards (e.g., SAE J2735 for V2X).
- Strong experience in data preparation and integration, including ETL, handling multimodal data (imagery, sensor data, time-series), and addressing data quality challenges.
- Strong analytical skills with familiarity in model evaluation metrics (e.g., AUC, accuracy) and testing AI systems under varied conditions.
- Excellent communication and collaboration skills, with experience in stakeholder engagement, technical reporting, and presenting complex AI concepts to non-technical audiences. Ability to travel up to 20% for meetings and site visits.
- Ability to obtain and maintain a Public Trust clearance.
- All applicants must be legally authorized to work in the United States without company sponsorship.
- Experience with state DOTs or federal transportation agencies on AI initiatives, including AI roadmaps, implementations, or evaluations in ITS.
- Experience in synthetic data generation, generative AI, or physics-informed ML for transportation applications.
- Knowledge of federal AI governance, risk management, and equity considerations in transportation.
- Project management experience, including leading AI…
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