Transportation Data Scientist

Leidos•McLean, VA

12d•$87,100 - $157,450•Onsite

About The Position

Leidos operates the Federal Highway Administration’s (FHWA) Saxton Transportation Operations Laboratory (STOL), a USDOT research lab focused on the improvement of transportation operations, safety, mobility, and environmental impacts. STOL provides a variety of services to support the advancement and deployment of emerging technologies, including vehicle automation and communication. Leidos is seeking a talented Transportation Data Scientist at the junior to mid-level to support FHWA-funded projects at the intersection of AI, data science, and transportation. This role will involve 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 exploring opportunities to apply AI in state-level transportation initiatives. This position offers the chance to contribute to innovation in a dynamic, federally supported research environment.

Requirements

Master’s degree in computer science, Data Science, Artificial Intelligence, Transportation Engineering, or a related field; Ph.D. preferred.
2+ years of professional experience (non-academic) 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).
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, site visits, or conference support.
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.

Nice To Haves

Prior experience working with state DOTs or federal transportation agencies (e.g., FHWA, USDOT) on AI initiatives, including prototyping and developing AI application in ITS.
Familiarity with transportation-specific data sources (e.g., HSIS, SHRP2, NGSIM) and standards (e.g., 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 (e.g., TRB, ITS America).

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.
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.