Transportation Data Scientist

Leidos • McLean, VA

7d • $87,100 - $157,450 • Onsite

About The Position

Are you interested in shaping the future of transportation? Consider joining the Leidos team operating FHWA’s 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 adoption of emerging technologies, including automation and communication in vehicles and on the roadside. Location: This role will be expected to work full-time at the customer site in McLean, VA Learn about STOL here : https://highways.dot.gov/turner-fairbank-highway-research-center/labs/STOL Candidates MUST: Be US Citizens or Green Card Holders who are currently in the United States for the current three consecutive years and be eligible for a Public Trust Clearance. Leidos is seeking a highly skilled Transportation Data Scientist to support FHWA-funded projects at the intersection of AI, data science, and transportation. This role will focus on developing and deploying 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 hands-on experience in AI model development, data integration, and stakeholder engagement, with a passion for identifying new opportunities to apply AI in state-level transportation initiatives. This position offers the chance to drive innovation in a dynamic, federally supported research environment.

Requirements

• Bachelor’s degree in computer science, Data Science, Artificial Intelligence, Electrical Engineering, Transportation Engineering, or a related field; Master's or Ph.D. preferred.
• 5+ years of professional experience in data science and AI/ML, with demonstrated expertise 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).
• Proven 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 experience 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.

Nice To Haves

• 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.
• Demonstrated hands-on experience proposing, designing, developing, and deploying AI systems within ITS, such as for freight classification, traffic crash prediction, or WIM data analysis.
• 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

• 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, 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.
• Lead 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.
• Identify and pursue 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.
• Provide technical leadership in monthly progress reporting, risk mitigation, and iterative model refinement based on federal feedback.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 11-50 employees