Transportation Data Scientist (AI Solutions)

About the role

Leidos operates the Federal Highway Administration’s (FHWA) Saxton Transportation Operations Laboratory (STOL), a USDOT research lab focused on improving transportation operations, safety, mobility, and environmental impacts. STOL supports the advancement and deployment of emerging technologies, including vehicle automation and communication. Leidos seeks a Transportation Data Scientist (junior to mid‑level) to support FHWA‑funded projects at the intersection of AI, data science, and transportation. The 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 position is full‑time at the customer site in McLean, VA. Candidates must be currently located in the United States for the last three consecutive years and be eligible for a Public Trust Clearance.

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, 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, 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.
• 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.
• Collaborate with cross‑functional teams to ensure project alignment with USDOT 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

• Master’s degree in computer science, Data Science, Artificial Intelligence, Transportation Engineering, or a related field; Ph.D. preferred.
• 2+ years of professional (non‑academic) experience in data science and AI/ML, with demonstrated familiarity in machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit‑learn), data processing tools (e.g., Pandas, NumPy), and AI techniques (e.g., deep learning, generative AI like GANs, computer vision, LLMs).
• Hands‑on experience in AI solution development.
• 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