Exploitation Specialist / Imagery Scientist, Expert

Avalore, LLC•Springfield, VA

2h•$205,000 - $250,000•Onsite

About The Position

This role is contingent upon award. Avalore, LLC is seeking an Exploitation Specialist / Imagery Scientist, Expert to support Department of War (DoW) customers in Springfield, VA. The Exploitation Specialist / Imagery Scientist, Expert will be SAR-focused and provide technical direction and conduct the work necessary to acquire and prepare imagery of the necessary quality, standards, and requirements provided by the Government. Developed solutions should be informed by specific phenomenology limitations and advantages of the sensors and platforms in mind.

Requirements

TS/SCI with CI Poly Eligibility
4+ years as a SAR expert with understanding of collection, phenomenology, image formation process, and exploitation products.
Exhibit experience using SARPy and Matlab SAR toolbox.
Experience in RNIIRS, information theoretic-based image quality metrics, SAR imagery quality metrics (e.g., Integrated Sidelobe Ratio, Multiplicative Noise Ratio), and sensor metadata describing impacts of geometry on phenomenology (e.g., graze, squint, azimuth).
Exhibit experience exploiting SAR to determine the occurrence and location of objects of interest.
Exhibit experience developing, testing, and evaluating new algorithms, processes, methodologies, and products using SAR imagery including experience utilizing advanced processing tools (e.g., Python, MATLAB, Google Earth Engine, etc.) to automate scientific processes of SAR imagery data to aid analysis.
Exhibit experience communicating with a variety of technical and non-technical audiences on availability and capabilities of SAR imagery products, methodologies, procedures, and algorithms to enhance analysis.
Exhibit a deep understanding of the principles of remote sensing and imagery processing and advanced exploitation methods.

Nice To Haves

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

Responsibilities

Integrate emerging sensors and platforms into existing Maven or Government directed data pipelines and workflows.
Develop pipelines and relevant data structures to incorporate new sensor source into existing data workflows, with as much available information as possible on the emerging sensor.
Conduct 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 development purposes. This may involve converting between file format types or tiling full-size images into specified sizes or geospatial bounds.
Investigate any gaps in the emerging sensor capability that may need to be supplemented by other sources.
Explore options for coincident imagery collects from other imagery platforms (e.g., EO platforms) that align with areas of emerging sensor's collection. Determine other platforms with similar geographic and temporal coverage.
Develop and implement mathematical conversion models to transform data labels from multiple imagery types (e.g., PNG to SICD, WBID, SIDD), as well as between orthorectified and non-orthorectified imagery.
Develop methods to execute tiling and pre-processing of full-size raw imagery into specified sizes and data formats (e.g., PNG) while maintaining metadata and data integrity.
Develop and implement mathematical conversion models to transform pre-processed tiles between image types and generate tiles with specified, precise geospatial boundaries. These methods should account for orthorectification and other geospatial aspects to ensure edges of the tile remain geospatially accurate. Generate tiles from full-size image collects that match specific pixel dimensions or geospatial boundaries.
Analyze and assess image quality and sensor metadata to make recommendations and curate imagery acquisition in alignment with Government directed priorities.
Execute day-to-day management of all imagery curation, acquisition, and pre-processing of imagery, such as image tiling, generation of pre-labels (i.e., labels generated from intelligence reporting, machine-derived observation, human observation, or other method that indicates the geospatial location and classification of an object according to a defined ontology used to tip and cue human labelers to an object of interest), and converting between file types in accordance with NGA Maven data strategies.
Generation of pre-labels derived from: Intelligence reporting, Machine-derived observations, Human observations, other methods identifying geospatial location and object classification. Pre-labels must conform to defined ontology standards and serve to tip and cue human labelers to objects of interest.
Provide EO imagery coincident with any SAR delivered imagery within a specified geographic and temporal range.
Identify methods to curate imagery according to Government directed prioritization. Examples of curation methods include using Natural Language Processing (NLP) to extract information from Intelligence Reporting, develop automated or semi-automated machine-based technologies to maximize likelihood of imagery with objects of interest or connect to intelligence repositories via API.
Monitor and prioritize data holdings to support data diversity needs defined by the Government (e.g., different geographic regions, temporal windows, metadata characteristics, scene characteristics, image sensor platforms).
Develop, test, and evaluate new algorithms, processes, methodologies, and products using remotely sensed data, imagery, and cloud-based solutions.
Oversee weekly deliveries of processed imagery and conduct ad-hoc deliveries as needed for quick-turn operations support.