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Sr. Geospatial Data Scientist; NGA - TS​/SCI

Job in Miami, Miami-Dade County, Florida, 33222, USA
Listing for: LaunchCode
Full Time position
Listed on 2026-02-16
Job specializations:
  • IT/Tech
    Data Analyst, Data Engineer
Salary/Wage Range or Industry Benchmark: 130000 - 140000 USD Yearly USD 130000.00 140000.00 YEAR
Job Description & How to Apply Below
Position: Sr. Geospatial Data Scientist (NGA - TS/SCI)

Job Title: Senior Geospatial Data Scientist

Position Type: Full-Time W2, Direct Hire

Hourly Pay: $130,000 - $140,000 + Benefits

Location
:
On-Site, Miami, Florida

Years of Experience

Required:

5

Relocation Stipend (if applicable): $2,500

Overview

This senior-level role focuses on transforming complex geospatial and intelligence data into actionable insights that directly support operational planning and decision-making. The position requires deep expertise in geospatial data science, advanced Python scripting, and large-scale data processing, along with a strong understanding of analytic standards and intelligence workflows. Working independently, the Senior Geospatial Data Scientist designs repeatable processes, uncovers meaningful patterns in complex datasets, and delivers clear, operationally ready visualizations that can be seamlessly integrated into daily mission activities.

Key Responsibilities
  • An understanding of Transnational Criminal Organizations (TCOs) operating within the USSOUTHCOM AOR
  • An understanding of coca cultivation and production areas, Familiarity with Intelligence Community Directive (ICD) 203 (Analytic Standards) and ICD 206
  • An understanding of how to operationalize the analytic output—shape/frame it in a way that supports operational planning
  • Must be able to operate at a senior level and in an independent environment.
  • Must be proficient at creating processes around large data sets to identify analytical discoveries,
  • Must be able to script at a Senior Level in Python.
  • Must be able to take these discoveries and provide them through visualizations that are easily integrated into daily operations.
Required

Skills & Qualifications
  • Active TS/SCI security clearance
  • 5 – 7 years of demonstrated programming skills with the ability to write/maintain scripts, including Python scripts.
  • An understanding of how to operationalize the analytic output—shape/frame it in a way that supports operational planning
  • Knowledge and experience using version control tools such as Git.
  • 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 conversion,s and knowledge of projections and their properties/conversions.
  • 1 - 3 years of experience, using Tableau or equivalent Business Intelligence software and software server functions.
Preferred Qualifications
  • Experience in combining digital cartography, computer technology, GIS, cartographic and geospatial production techniques, remote sensing photography, and digital data formats.
  • Proficient in Feature Manipulation Engine (FME) software such as FME Workbench
  • Experience performing data analysis to examine data from a variety of angles to determine hidden values, weaknesses, trends, and or opportunities. Next line knowledge of ESRI, ArcGIS/Arc Pro, and Arc Server
  • Experience with ESRI Workflow Manager, WMX, and TAM
  • Knowledge of database systems and architecture
  • Ability to write SQL and No

    SQL
  • Understanding of cloud architecture, infrastructure, services (including geospatial-specific microservices), and Dev Ops
  • Knowledge of symbolization rules.
  • Experience working with geospatial data in a multiuser enterprise environment.
  • 2 years of experience working with data quality control, tools, including ArcGIS data reviewer
  • Experience with analyzing existing production systems and providing feature recommendations to enhance data science capabilities
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