Transportation Data Scientist; Jr level Job McLean area,Virginia USA,IT/Tech

Position: Transportation Data Scientist (Jr level)

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 (WIM) data and imagery, crash prediction in traffic management centers (TMCs), 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: (Use the "Apply for this Job" box below)./STOL

Candidates MUST

All applicants must be legally authorized to work in the United States with proof of legal status and be eligible for a Public Trust Clearance, which includes three (3) consecutive years in the United States within the last five (5) years.

Primary Responsibilities

Assist in conducting data and literature reviews, including targeted searches for AI methods, datasets, and technologies relevant to freight analytics, traffic safety, and operations (e.g., sensor fusion, computer vision, and multimodal AI).

Prepare and integrate datasets for AI use cases, including cleaning, normalizing, enriching, and fusing multi-source data (e.g., traffic logs, imagery, weather, and permitting records) while addressing quality issues like inconsistency, sparsity, and bias.

Contribute to the design, development, and deployment of AI/ML models for transportation applications, including classifying oversized/overweight (OS/OW) vehicles using WIM data and imagery, crash prediction in TMCs, and generating synthetic data for model training.

Evaluate AI model performance under diverse conditions, such as varying data quality levels, and provide recommendations for improving model 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 to gather insights on AI applications in WIM systems, permitting integration, and crash prediction.

Assist in identifying and pursuing new opportunities with state DOTs for AI initiatives, including contributions to developing roadmaps, proposals, and implementation strategies for AI in ITS, such as anomaly detection, traffic optimization, and safety analytics.

Collaborate with cross-functional teams to ensure project alignment with FHWA goals, including risk management, quality assurance, and compliance with federal standards.

Contribute to monthly progress reporting, risk mitigation, and iterative model refinement based on federal feedback.

Required Qualifications

Bachelor's degree in computer science, Data Science, Artificial Intelligence, Electrical Engineering, Transportation Engineering, or a related field;
Master's preferred.
Must have a 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).
Must have 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 processes, handling multimodal data (e.g., imagery, sensor data, time-series), and addressing data quality challenges in real-world applications.
Strong analytical skills with familiarity in model evaluation metrics (e.g., AUC, accuracy, scalability) 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 work in a fast-paced, research-oriented environment with travel up to 20% for stakeholder meetings and site visits.
Ability to obtain and maintain a Public Trust clearance (which includes three years of immediate residency in the US).
All applicants must be legally authorized to work in the United States without company sponsorship.

Preferred Qualifications

Prior experience working with state DOTs or federal transportation agencies (e.g., FHWA, USDOT) on AI initiatives, including developing AI roadmaps, implementations, or evaluations in ITS.
Experience in synthetic data generation, generative AI (e.g., LLMs), or physics-informed ML for transportation applications.
Knowledge of federal AI governance, risk management, and equity considerations in transportation.
Project management experience,…


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