Transportation Data Scientist

Are you interested in shaping the future of transportation Consider joining the Leidos team operating FHWAs 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 requires full-time on-site work at the customer site in McLean VA.

Learn about STOL here: MUST:

All applicants must be legally authorized to work in the United States with proof of legal status and be eligible for a Public Trust Clearance which includes three consecutive years in the United States within the last five years.

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.

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
Bachelors degree in computer science Data Science Artificial Intelligence Electrical Engineering Transportation Engineering or a related field; Masters 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).

• Must have experience collaborating with state and local DOTs - developing new customers / Business Development experience

• Must have a minimum of at least 2 years of relevant non-academic experience.

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.

Preferred Qualifications
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).

Anticipated salary range for this role is $95000-$130000

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

Were the tech company everyone calls when things get weird. We dont wear capes (theyre a safety hazard) but we do solve high-stakes problems with code caffeine and a healthy disregard for how its always been done.

Original Posting:

Pay Range:

The Leidos pay range for this job level is a general guideline onlyand 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.