Imagery Scientist - Electro-optical (EO)

Bridge Defense•Alexandria, VA

20h•$100,000 - $155,000•Onsite

About The Position

Bridge Defense is seeking several Imagery Scientist - Electro-optical (EO) for full-time salaried positions supporting an Intelligence Community partner. These positions are currently planned for first shift. The EO Geospatial Analyst shall provide geospatial and imagery expertise and quantitative analysis to make recommendations that improve data curation and development in support of Machine Learning algorithm testing and evaluation. Leverage multi-INT data, with an emphasis on EO, 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 identifying data gaps to develop a diverse, representative dataset for Machine Learning model evaluation. Assist the EO lead in conducting assessment of potential differences between new sensor characteristics and capabilities compared to currently utilized platforms. Assess potential differences in metadata, data format, and data structure characteristics in regards to changes to databases, schemas, APIs, and other ETL related processes for the ingestion and movement of data when integrating into the existing data operations pipeline. Determine how to acquire new data, potential latency associated with acquisition, data formats, and security domains. Determine how to pre-process and standardize the data to match existing data standards or to be transformed into a usable state for labeling and model testing purposes. This may involve converting between file format types or tiling full-size images into specified sizes or geospatial bounds.

Requirements

Experience with Electro-optical 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.
Excellent oral, writing, and coordination skills to communicate factual information clearly and concisely, both orally and in writing, often under pressure and tight deadlines that meets or exceeds established analytical standards.
TS/SCI Clearance (with CI Poly, or willingness to sit for CI Poly)

Nice To Haves

Experience applying CV and machine learning (ML) techniques to EO imagery and data to address intelligence problems.
Strong risk management skills.
Excellent communication, teamwork, and conflict management skills.
Analytical and effectively able to prioritize needs, requirements, and other issues.
Ability to communicate and interact effectively at all levels of staff and management.
Ability to exercise independent judgment, develop relationships, and obtain consensus among interested parties.
Critical thinker with strong technical skills, diagnostic skills and problem-solving ability.
Solid written and verbal communication skills to negotiate direction, drive projects and projects to successful conclusion and deliver knowledge to team members verbally and via clear designs, runbooks and technical engineering and exchange sessions.
Self-starter, flexible, adaptable, collaborative and motivated to champion continuous improvement.
Ability to develop peer networks across an enterprise to maintain technology awareness and to support resolution of problems.
Ability to operate across traditional technical boundaries, comfortable working in the compute space as well as the storage space in an operational capacity.
Technically curious and driven to learn new skills.
Willing to travel, if needed.

Responsibilities

Provide geospatial and imagery expertise and quantitative analysis to make recommendations that improve data curation and development in support of Machine Learning algorithm testing and evaluation.
Leverage multi-INT data, with an emphasis on EO, to curate imagery from the emergent sensors.
Prioritize curated imagery for creation of labeled data to support model evaluation and accreditation.
Recommend curation strategies and identify data gaps based on emergent sensor characteristics and limitations to develop a diverse, representative dataset for Machine Learning model evaluation.
Assist the EO lead in conducting assessment of potential differences between new sensor characteristics and capabilities compared to currently utilized platforms.
Assess potential differences in metadata, data format, and data structure characteristics in regards to changes to databases, schemas, APIs, and other ETL related processes for the ingestion and movement of data when integrating into the existing data operations pipeline.
Determine how to acquire new data, potential latency associated with acquisition, data formats, and security domains.
Determine how to pre-process and standardize the data to match existing data standards or to be transformed into a usable state for labeling and model testing purposes.