Transportation Data Scientist (AI Solutions)

Are you interested in improving and shaping the transportation industry in a group of intelligent and motivated individuals?  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. Location: This role will be expected to work full-time at the customer site in McLean, VA Candidate MUST: Be currently located in the United States for the current three consecutive years and be eligible for a Public Trust Clearance. https://careers.leidos.com/search/jobs?q=stol&ns_job_category=stol-jobs Primary 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.

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). Must have HANDS-ON experience in AI Solution Development. 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