Senior Data Scientist (Machine Learning & Geospatial Analytics) – TS/SCI Required

Leidos•Springfield, VA

21h

About The Position

Leidos is seeking a Senior Data Scientist with strong machine learning development and coding expertise to support a customer mission in Springfield, VA. Russian language proficiency is a strong plus, but not required. An active TS/SCI clearance with willingness to obtain a Polygraph is required to be considered. This position is focused on advanced data science and machine learning development within a GEOINT mission space. The ideal candidate brings strong coding, model development, and analytic problem-solving skills, with the ability to integrate geospatial and multi-source data to deliver mission impact. Russian language and geospatial linguistic experience are considered a plus, but not required. The selected candidate will exploit, analyze, and produce imagery-derived intelligence products while integrating foreign geographic names data, native-language sources, and geospatial metadata to enhance analytic accuracy and mission impact. The analyst will work closely with Intelligence Community (IC) partners, geographic names experts, and multi-INT analysts to ensure imagery-derived assessments are geospatially precise, linguistically accurate, and operationally relevant. Accuracy, analytic rigor, and mission responsiveness are essential.

Requirements

Active TS/SCI clearance with willingness to obtain a Polygraph.
Bachelor’s degree and 12+ years of relevant experience, or Master’s degree and 10+ years of relevant experience in Data Science, Computer Science, Analytics, or related field. Additional experience may be considered in lieu of degree.
Demonstrated senior-level experience designing, training, and deploying machine learning models.
Strong coding skills in Python (or similar), with experience building scalable and maintainable solutions.
Demonstrated experience applying data science methodologies (machine learning, statistical analysis, data engineering) to real-world problems.
Experience structuring and processing large, complex, and unstructured datasets.
Experience processing unstructured data and documents (e.g., text, reports, open-source content).
Experience developing or supporting NLP capabilities, including querying across aggregated data sources.
Strong understanding of data pipelines, feature engineering, and model evaluation techniques.
Experience working with geospatial data and integrating spatial context into analytic workflows.
Strong research, critical thinking, and analytic writing skills.
Familiarity with working in high-side (classified) environments.
Experience operating effectively in fast-paced, mission-driven environments both independently and as part of a team.
Familiarity with working in high-side (classified) environments.

Nice To Haves

Russian language proficiency (ILR 2+ or higher).
Experience working with geospatial linguistics, toponymy, or foreign geographic data.
Regional expertise in Russian Federation and/or Russian-influenced geographies.
Experience developing machine learning models or AI-enabled analytics within national security or GEOINT environments.
Proficiency in Python for advanced analytics, automation, or data engineering workflows.
Experience developing ontologies, schemas, or knowledge graphs.
Experience integrating multi-INT or multi-source data into advanced analytic workflows.
Prior experience briefing senior government stakeholders.

Responsibilities

Design, develop, train, and deploy machine learning models to solve complex mission problems.
Write production-quality code in Python (or similar) to support model development, testing, and deployment.
Structure disparate and unstructured data (imagery, text, geospatial features) into usable formats for quantitative analysis and fusion.
Develop and maintain scalable data pipelines to support automated analytic workflows.
Build and apply statistical models, machine learning algorithms, and data processing techniques for pattern detection and predictive analysis.
Generate automated workflows to improve efficiency, reproducibility, and scalability of analytic production.
Aggregate existing data stores and enable natural language processing (NLP) query capabilities using existing APIs.
Process and analyze large volumes of unstructured data and documents to extract mission-relevant insights.
Integrate geospatial and multi-source data into advanced analytic workflows.
Translate complex quantitative findings into clear, actionable insights through visualization and storytelling.
Collaborate with cross-functional teams to deliver scalable, mission-focused data science solutions.
Brief findings clearly and confidently to technical and non-technical audiences.