Data Scientist – SME

Leidos

. Serve as the senior technical authority for data science, advanced analytics, and AI/ML .

Posted 10/15/2026full-timeUnited StatesSeniorLead💰 $131,300 - $237,350 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in data science, advanced analytics, and AI/ML, with a strong focus on developing and operationalizing machine learning models, particularly in geospatial contexts. Proven ability to lead technical teams, mentor peers, and communicate complex findings to stakeholders effectively.

Highest-signal resume keywords

Machine Learning Model DevelopmentGeospatial Analytics ExpertisePython and R ProficiencyBig Data Processing ToolsActive Top Secret/SCI Clearance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Statistical AnalysisPredictive ModelingAI/ML AlgorithmsData VisualizationFeature EngineeringModel ValidationAnomaly DetectionNatural Language ProcessingData Quality GovernanceOperational Effectiveness

Soft Skills

LeadershipCommunicationCollaborationProblem-SolvingMentoring

Tools & Technologies

GISArcGISTableauPowerBISparkPySparkKafkaHadoopAWSAzure

Industry Keywords

Geospatial Data FormatsRemote Sensing TechniquesIntelligence CommunityData Science StandardsAgile Methodologies

Tech Stack

Tools & technologies

AWSAzureHadoopKafkaNumpyPandasPySparkPythonPyTorchRemote SensingScikit-LearnSparkTableauTensorflow

About the role

Key responsibilities & impact

Serve as the senior technical authority for data science, advanced analytics, and AI/ML
Lead design, development, validation, and optimization of statistical, predictive, prescriptive, and machine learning models
Analyze complex heterogeneous datasets to identify patterns, anomalies, trends, and insights supporting mission objectives
Drive development and operationalization of AI/ML capabilities including classification, regression, clustering, anomaly detection, NLP, and predictive modeling
Establish standards and best practices for model performance, validation, explainability, bias, uncertainty, reproducibility, and operational effectiveness
Partner with data, software, and systems engineers on data requirements, feature engineering, data quality, governance, and scalable integration
Collaborate with engineering and DevSecOps teams to transition analytical solutions into secure, maintainable, scalable production capabilities and CI/CD pipelines
Support Agile and SAFe planning and execution by translating data science initiatives into capabilities, stories, technical enablers, dependencies, and risks
Lead technical reviews, trade studies, and working sessions
Engage customers, stakeholders, and leadership to communicate findings, recommendations, risks, and mission impact
Mentor data scientists, engineers, and other technical team members; review analytical approaches, code, models, and documentation
Evaluate and recommend emerging data science and AI/ML technologies

Requirements

What you’ll need

US citizenship is required per contract
Active Top Secret/SCI clearance with the ability to successfully pass a Polygraph examination
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
12+ years demonstrated experience working as a Data Scientist, Senior/Principal Data Scientist, Machine Learning Engineer, or equivalent senior technical role
Extensive experience applying statistical analysis, machine learning, and advanced analytics to complex problems
Proven expertise in developing and implementing machine learning and AI models, particularly in geospatial or remote sensing
Deep knowledge of geospatial analytics tools (e.g., GIS, ArcGIS), remote sensing techniques, and application of data science in the IC
Expert proficiency in Python and/or R (or similar languages) and data science libraries including Pandas, NumPy, SciPy, Scikit-learn, TensorFlow, or PyTorch
Strong experience with big data processing tools such as Spark, PySpark, Kafka, Hadoop, AWS, or Azure
Expertise with geospatial data formats such as GeoTIFF, Shapefiles, WMS, and WFS and spatial libraries such as GeoPandas, Rasterio, and GDAL
Advanced experience developing and operationalizing AI/ML models and algorithms for geospatial data
Strong background in data visualization and reporting tools such as Tableau and PowerBI
Strong leadership, communication, and collaboration skills
Excellent problem-solving and analytical skills with ability to translate technical challenges into actionable insights