GIS Data Analyst

Radiance Technologies is an employee-owned company with benefits that are unmatched by most companies in the Dayton, OH area. Employee ownership, generous 401K, full health/dental/life/vision insurance benefits, educational reimbursement, competitive salaries, interesting assignments, and a pleasant work environment combine to make Radiance Technologies a great place to work and succeed.

Radiance is seeking a highly qualified and motivated data scientist to support geospatial intelligence production and the continued advancement of data analytical processes and techniques at the National Air and Space Intelligence Center (NASIC).

The successful candidate is someone who has extensive experience working in a geographic information system framework and can creatively approach a variety of requirements to meet intelligence needs, can balance work on multiple tasks, and will work collaboratively with other scientists, engineers, and analysts.

Job Duties

Utilize advanced data science methods to analyze and exploit GEOINT data and other sources of supplementary data in a GIS framework to support intelligence production

Assist in developing approaches to address key intelligence questions

Required Skills:

• Active Top Secret Clearance with SCI eligibility
• Substantial familiarity with ArcGIS software
• Experience designing, developing, and deploying dynamic geospatial web applications and tools
• Strong analytical, inference, critical thinking, and creative problem-solving skills
• Ability to work both independently and in a team environment
• Strong technical writing and briefing skills undefined
• Desired Qualifications: ·

Proficiency in developing custom solutions using ArcGIS Online, ArcGIS Portal, and ArcGIS Enterprise, with a specific focus on ArcGIS Experience Builder, ArcGIS Dashboards, and Survey123

• Ability to code/script, especially in Python
• Experience with data science and visualization tools
• Background in machine learning/artificial intelligence
• Experience with GEOINT data, including non-literal data
• Knowledge of remote sensing datasets