Geospatial Mission Solutions Data Scientist
LeidosSpringfield, VAPosted 9 March 2026
Job Description
The Leidos Geospatial Mission Solutions Portfolio is seeking a change and thought leader in the field of geospatial data science. This is an exciting opportunity to elevate critical national-level missions through innovation, data-driven insight, and advanced analytic solutions.
As a Geospatial Mission Solutions Data Scientist, you will help shape the next generation of geospatial capabilities by applying data science to real-world intelligence challenges. This position straddles the worlds of direct customer mission support and Leidos-driven innovation, blending hands-on problem solving with forward-looking solution development.
We’re looking for someone who thrives at the intersection of technology, analytics, and mission, and who is passionate about transforming geospatial data into actionable intelligence that drives smarter decisions and stronger outcomes.
Staffing Requirement
The work requires staff to provide the customer with data science and automation support. Staff will collect, process, and perform data analysis and deliver data driven solutions to quantitatively improve multiple mission areas. They will perform deep investigations on complex problems using applied statistics, building algorithms, and collaborating with teammates and customers to use their domain expertise to convey complicated realities to non-technical audiences. Any data management solutions created should take into consideration any modernization objectives, including modernized database applications as well as other enterprise activities.
Duties :
Provide embedded GEOINT/ Data Science expertise to assure continuity of data provision to the customer.
Statistical analysis and modeling of in-house created data.
Automating repetitive data management tasks.
Clean, convert, migrate, and organize data.
Perform ad hoc analysis and present results in a clear manner.
Communicate complex quantitative analysis in a clear, precise and actionable manner using storytelling/visualization. This may include data visualization of disparate sources via dashboards that can be understood by nontechnical audiences.
Create/deploy/maintain tools to extract features data and meta-data from a variety of sources, including rasters, vectors, text, and other source databases.
Write/maintain Python scripts for various environments and software including ArcGIS to improve efficiency of the production process.
Deliver service solutions that integrate best practices to modernize current practices.
Perform data Investigations on complex problems using applied statistics, building algorithms and collaborating with domain expertise to convey complicated realities to non-technical audience.
Ensure new workflows can be integrated seamlessly with modernized database applications and enterprise-level APls.
Data visualization of production metrics of in-house created data.
Skills and Experience
Required
Typically requires a BA degree and 8 – 12 years of prior relevant experience or Masters with 6 – 10 years of prior relevant experience.
5 – 7 years of demonstrated programming skills with the ability to write/maintain scripts, including Python and JavaScript
3-5 years’ experience in data science, including the areas of data collection processing exploitation, transformation services, modeling, and analytics.
3-5 years of demonstrated experience performing geospatial data management, including data type conversions coordinate systems in (latitude and longitude, UTM) and their conversions and knowledge of projections and their properties/conversions.
Knowledge and experience using version control tools such as Git
Knowledge and experience using GIS tools such as QGIS and/or ArcGIS
Knowledge and experience using PostgreSQL, Postgis, PgAdmin
Knowledge and experience using Application programming interfaces (APls)
Knowledge and experience using applied statistics
Knowledge and experience building algorithms
Desired
Experience in combining digital cartography, ... (truncated, view full listing at source)
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