Data Scientist SME
About The Position
Leidos is seeking a highly accomplished Data Scientist Subject Matter Expert (SME) for the Chinook Program, supporting an advanced application for an Intelligence Community customer. The role combines data science, software engineering, artificial intelligence, machine learning, analytics, and mission operations to turn complex data into operational capabilities and mission-critical insights. The SME will lead the design, development, evaluation, and operationalization of sophisticated data science capabilities. The position requires both strategic leadership—establishing data science strategy and standards—and hands-on technical work addressing complex analytical and engineering challenges. The successful candidate will work closely with software, data, systems, DevSecOps, UI/UX, product, test, program leadership, and intelligence customer stakeholders. The role may be performed in Tucson, Arizona; Gaithersburg; Alexandria or Chantilly, Virginia; Englewood, Colorado; or St. Louis, Missouri. An active Top Secret/SCI clearance and the ability to pass a polygraph are required.
Requirements
- U.S. citizenship is required by contract.
- Bachelor’s degree in Data Science, Computer Science, Geospatial Science, or a related field with 12–15 years of relevant experience, or a master’s degree with 10–13 years of relevant experience; a doctorate in a technical domain may be held.
- At least 12 years of experience as a Data Scientist, Senior/Principal Data Scientist, Machine Learning Engineer, or in an equivalent senior technical role.
- Extensive experience applying statistical analysis, machine learning, and advanced analytics to complex problems.
- Proven expertise developing and implementing machine learning and AI models, particularly for geospatial or remote-sensing applications.
- Deep knowledge of geospatial analytics tools such as GIS and ArcGIS, remote sensing techniques, and data science applications in the Intelligence Community.
- Expert proficiency in Python and/or R (or similar languages) and experience with data science libraries such as Pandas, NumPy, SciPy, Scikit-learn, TensorFlow, or PyTorch, or equivalent technologies.
- Strong experience with big-data processing tools such as Spark, PySpark, Kafka, Hadoop, and AWS or Azure cloud platforms.
- Expertise working 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, such as satellite imagery object detection, spatial clustering, and predictive analytics.
- Strong background in data visualization and reporting tools such as Tableau and Power BI.
- Strong leadership, communication, and collaboration skills, including the ability to work directly with senior government officials, analysts, and technical teams.
- Excellent problem-solving and analytical skills, with demonstrated ability to translate technical challenges into actionable insights.
- Active Top Secret/SCI clearance and ability to successfully pass a polygraph examination.
Nice To Haves
- Advanced certifications in data science or machine learning.
- Familiarity with the customer’s mission and specific geospatial intelligence challenges.
- Expertise in advanced deep learning, particularly computer vision and image analysis for geospatial applications.
- Experience with cloud-native applications and distributed computing in a geospatial context.
- Experience deploying models into production environments.
- Experience with Kubernetes and containerized applications.
- Experience developing REST APIs or other services for analytic capabilities.
- Experience with NLP and/or LLMs.
- Experience with geospatial analytics and spatial data.
- Experience with streaming or near-real-time analytics.
- Experience developing analytical capabilities in secure or air-gapped environments.
- Published research or technical papers in geospatial intelligence, machine learning, or data science.
Responsibilities
- Serve as the senior technical authority for data science, advanced analytics, and AI/ML, translating mission needs into actionable analytical solutions.
- Lead the design, development, validation, and optimization of statistical, predictive, prescriptive, and machine learning models.
- Analyze complex, heterogeneous datasets to identify patterns, anomalies, trends, and insights that support 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 to define data requirements, feature engineering, data quality, governance, and scalable integration approaches.
- 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, and establish methodologies, patterns, and practices across the program.
- Engage with customers, stakeholders, and leadership to communicate findings, recommendations, risks, and mission impact.
- Mentor data scientists, engineers, and other technical team members, and review analytical approaches, code, models, and documentation.
- Evaluate and recommend emerging data science and AI/ML technologies to improve application capabilities and mission outcomes.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
