Transportation Data Scientist

Job Description

Description

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.

Learn about STOL here : -fairbank-highway-research-center / labs / STOL

Leidos is seeking a highly skilled Senior 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.

Location : This role will be expected to work full-time at the customer site in McLean, VA

Primary 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.

Required Qualifications

Preferred Qualifications

Anticipated salary range for this role is $95,000-$130,000

Come break things (in a good way). Then build them smarter.

We're the tech company everyone calls when things get weird. We don’t wear capes (they’re a safety hazard), but we do solve high-stakes problems with code, caffeine, and a healthy disregard for “how it’s always been done.”

Original Posting : October 3, 2025

For U.S. Positions : While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.

Pay Range : Pay Range $85,150.00 - $153,925.00

The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.