Transportation Data Scientist

Location: Full‑time at the customer site in McLean, VA.

• Conduct 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, cleaning, normalizing, enriching, and fusing multi‑source data (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, recommending improvements for robustness, scalability, and trustworthiness in real‑world transportation environments.
• Support stakeholder outreach and engagement, 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.

• 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 familiarity in machine learning frameworks (Tensor Flow, PyTorch, Scikit‑learn), data processing tools (Pandas, Num Py), and AI techniques (deep learning, generative AI like GANs, computer vision, LLMs).
• Strong experience in data preparation and integration, including ETL processes, handling multimodal data (imagery, sensor data, time‑series), and addressing data quality challenges in real‑world applications.
• Strong analytical skills with familiarity in model evaluation metrics (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, site visits, or conference support.
• Ability to obtain and maintain a Public Trust clearance (which includes three years of immediate residency in the U.S.).
• All applicants must be legally authorized to work in the United States.

• Prior experience working with state DOTs or federal transportation agencies (e.g., FHWA, USDOT) on AI initiatives, including prototyping and developing AI applications in ITS.
• Familiarity with transportation‑specific data sources (HSIS, SHRP2, NGSIM) and standards (SAE J2735 for V2X).
• 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, including leading AI tasks in multi‑agency initiatives or contributing to communities of practice (CoPs).
• Publications or presentations in AI/transportation conferences (TRB, ITS America).

Compensation: Anticipated salary range is $100,000–$120,000.

Equal Opportunity: All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy, family structure, marital status, ancestry, domestic partner status, sexual orientation, gender identity or expression, veteran or military status, or any other basis prohibited by law. Leidos will also consider for employment qualified applicants with criminal histories consistent with relevant laws.