Senior Data Scientist (Machine Learning & Geospatial Analytics) - TS/SCI Required in Springfield

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.

Leidos is seeking a Senior Data Scientist with strong machine learning development and coding expertise to support a customer mission in Springfield, VA. Russian proficiency is a strong plus, but not required.

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An active TS/SCI clearance with willingness to obtain a Polygraph is required to be considered.

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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 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, - sources, and geospatial metadata to enhance analytic accuracy and mission impact., Accuracy, analytic rigor, and mission responsiveness are essential., 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. \n

• \n Demonstrated senior-level experience designing, training, and deploying machine learning models. \n

• \n Strong coding skills in Python (or similar), with experience building scalable and maintainable solutions., Experience working with geospatial data and integrating spatial context into analytic workflows., Experience operating effectively in fast-paced, mission-driven environments both independently and as part of a team.

Write production-quality code in Python (or similar) to support model development, testing, and deployment. \n

• \n Structure disparate and unstructured data (imagery, text, geospatial features) into usable formats for quantitative analysis and fusion. \n

• \n Develop and maintain scalable data pipelines to support automated analytic workflows. \n

• \n Build and apply statistical models, machine learning algorithms, and data processing techniques for pattern detection and predictive analysis. \n

• \n Generate automated workflows to improve efficiency, reproducibility, and scalability of analytic production. \n

• \n Aggregate existing data stores and enable natural processing (NLP) query capabilities using existing APIs. \n

• \n Process and analyze large volumes of unstructured data and documents to extract mission-relevant insights. \n

• \n Integrate geospatial and multi-source data into advanced analytic workflows. \n

• \n Translate complex quantitative findings into clear, actionable insights through visualization and storytelling. \n

• \n Collaborate with cross-functional teams to deliver scalable, mission-focused data science solutions. \n

• \n Brief findings clearly and confidently to technical and non-technical audiences. \n

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\n, * \n Demonstrated experience applying data science methodologies (machine learning, statistical analysis, data engineering) to real-world problems. \n

• \n Experience structuring and processing large, complex, and unstructured datasets. \n

• \n Experience processing unstructured data and documents (e.g., text, reports, open-source content). \n

• \n Experience developing or supporting NLP capabilities, including querying across aggregated data sources. \n

• \n Strong understanding of data pipelines, feature engineering, and model evaluation techniques. \n