Turn complex data into mission advantage. Join Leidos on the Chinook Program supporting our Intelligence Community customer and play a critical role in transforming massive, complex datasets into actionable intelligence and mission insights. As a Data Scientist SME, you will bring advanced analytical expertise, innovative thinking, and technical leadership to a high performing Agile engineering team delivering next generation capabilities to our Intelligence Community customer.

This is an opportunity to work at the intersection of data science, artificial intelligence, machine learning, cloud technology, and national security, where your expertise can directly influence how mission users understand data, identify patterns, and make critical decisions. You will serve as a technical authority while collaborating with software engineers, DevSecOps professionals, system architects, analysts, and mission stakeholders to solve challenging problems that don’t have off-the-shelf answers.

If you are energized by applying advanced analytics to real-world mission challenges and want to see your work have a tangible impact, this is an opportunity to make a difference.

Leidos National Security Sector combines technology-enabled services and mission software capabilities in the areas of cyber, logistics, security operations, and decision analytics to support our defense and intel customers’ mission to defend against evolving threats around the world. Our team’s focus is to ensure our customers have the right tools, technologies, and tactics to keep pace with an ever-evolving security landscape and succeed in their pursuit to protect people and critical assets.

Position may be performed in the following locations below.  Please note Gaithersburg, MD is the primary work location for the program.

• Gaithersburg, MD
• Alexandria, VA
• Chantilly, VA 
• Aurora, CO
• St. Louis, MO

Clearance Level Required:

Active Top Secret/SCI clearance with the ability to successfully pass a Polygraph examination.

Primary Responsibilities:

• Serve as the technical SME in data science and geospatial analytics, providing high-level guidance on complex technical problems related to the GEOINT mission.
• Lead the design and development of sophisticated data science models and machine learning algorithms to analyze large, multi-source geospatial data sets (e.g., satellite imagery, sensor data, geospatial databases).
• Apply deep knowledge of GEOINT, remote sensing, and spatial analysis to develop and implement solutions that enhance data-driven decision-making capabilities.
• Provide strategic recommendations for leveraging new and emerging technologies, including machine learning, AI, and cloud platforms, to improve analytical workflows, efficiency, and mission outcomes.
• Mentor and train junior data scientists and analysts, ensuring the application of best practices in data science and the development of mission-relevant expertise.
• Work closely with GEOINT analysts, program managers, engineers, and other stakeholders to identify critical requirements, define technical approaches, and ensure that solutions align with mission objectives.
• Conduct applied research to explore innovative data science techniques and emerging trends (e.g., deep learning, reinforcement learning, advanced geospatial algorithms) that can be applied to solve real-world intelligence problems.
• Oversee the deployment and operationalization of data science models, ensuring they are scalable, reliable, and deliver actionable insights to GEOINT decision-makers.
• Maintain high-quality documentation for models, methodologies, and analysis processes to support reproducibility, training, and knowledge transfer.

Basic Qualifications:

• US citizenship is required per contract.
• Bachelor’s degree in Data Science, Computer Science, Geospatial Science or related field and 12-15 years of prior relevant experience or Master’s with 10-13 years of prior relevant experience. May possess a Doctorate in technical domain.
• 10+ years of professional experience in data science.
• 5+ years of experience with GEOINT or geospatial data analysis.
• Proven expertise in developing and implementing machine learning and AI models, particularly in context of geospatial or remote sensing.
• Deep knowledge of geospatial analytics tools (e.g., GIS, ArcGIS), remote sensing techniques, and the application of data science in the IC.
• Expert proficiency in Python (or similar languages) and experience with data science libraries (Pandas, NumPy, SciPy, Scikit-learn, TensorFlow, PyTorch, or equivalent tecchnologies).
• Strong experience with big data processing tools (e.g., Spark, PySpark, Kafka, Hadoop, AWS or Azure cloud platforms).
• Expertise in working with geospatial data formats (e.g., GeoTIFF, Shapefiles, WMS, WFS) and spatial libraries (e.g., GeoPandas, Rasterio, GDAL).
• Advance experience in developing and operationalizing AI/ML models and algorithms for geospatial data (e.g., object detection from satellite imagery, spatial clustering, predictive analytics).
• Strong background in data visualization and reporting tools (e.g., Tableau, PowerBI).
• Strong leadership, communication, and collaboration skills, with the ability to work directly with senior government officials, analysts, and technical teams.
• Excellent problem-solving and analytical skills with a demonstrated ability to translate technical challenges into actionable insights.

Preferred Qualifications:

• Advanced certifications in data science or machine learning.
• Familiarity with the customer’s mission and specific geospatial intelligence challenges.
• Expertise in advanced deep learning techniques, particularly in computer vision and image analysis for geospatial applications.
• Experience with cloud-native applications and distributed computing in a geospatial context.
• Familiarity with satellite data analysis, and remoting sensing models.
• Published research or technical papers in geospatial intelligence, machine learning, or data science.

#Chinook

#NSBA

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Original Posting:

Pay Range:

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