Junior Transportation Data Scientist

Leidos is seeking a talented Junior Transportation Data Scientist at the junior 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

Learn about STOL here: 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.

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 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.
Assist in identifying and pursuing 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.
Contribute to 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 preferred.
2 years of professional experience 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).
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 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.
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 $75000-$100000 depending on experience.

If youre looking for comfort keep scrolling. At Leidos we outthink outbuild and outpace the status quo because the mission demands it. Were not hiring followers. Were recruiting the ones who disrupt provoke and refuse to fail. Step 10 is ancient history. Were already at step 30 and moving faster than anyone else dares.

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.