SAR Imagery Scientist

MANTECH•Springfield, VA

1d•$91,600 - $153,800•Onsite

About The Position

MANTECH seeks a motivated, career and customer-oriented SAR Imagery Scientist to join our team in Springfield, VA. This is a full-time onsite position. This role involves leveraging multi-INT data, with an emphasis on SAR, to curate imagery from emergent sensors. The curated imagery will be prioritized for the creation of labeled data to support model evaluation and accreditation. The role also requires recommending curation strategies and identifying data gaps to develop a diverse, representative dataset for Machine Learning model evaluation, based on the characteristics and limitations of the emergent sensor.

Requirements

6+ years of experience; BA/BS can account for 4 years while a MA/MS can account for 6 years of experience.
4+ years as a SAR expert with understanding of collection, phenomenology, image formation process, and exploitation products.
An active TS/SCI clearance with the ability to obtain and maintain a polygraph
Must be able to remain in a stationary position for extended periods while performing imagery analysis and reporting.
Must be able to occasionally move about the work area to collaborate with team members and access mission systems.
Must be able to communicate effectively with co-workers, management, and customers to exchange accurate information and present analytic findings.

Nice To Haves

Experience applying CV and machine learning (ML) techniques to SAR imagery and data to address intelligence problems

Responsibilities

Experience with Synthetic Aperture Radar imagery analytical products and conducting multi-INT analysis
Utilize structured, unstructured, and semi-structured data and visualization tools to exploit data through use of programming and scripting, including advanced geospatial skills and an excellent understanding of how to apply evolving technology and methodologies to related issues
Leverage multi-INT data, with an emphasis on SAR, to curate imagery from the emergent sensors. Curated imagery will be prioritized for creation of labeled data to support the model evaluation and accreditation. Curated imagery should have objects of interest or needed characteristics (e.g., geographic coverage, scene attributes) per Government prioritization.
Based on the characteristics and limitations of the emergent sensor, recommend curation strategies and identify data gaps to develop a diverse, representative dataset for Machine Learning model evaluation