Principal Analyst — BlueLens Analytics
Project Maven Principal Lead. NGA GS-14 Senior Analyst. JSOC embedded. Thirty years of operational geospatial intelligence experience — from digitizing paper maps in 1993 to deploying production computer vision systems at national security scale. Now operating as a cleared small business available for subcontracting, teaming, and senior consulting engagements.
Prime contractors win the vehicles. Then they need cleared senior people to actually do the work. The problem: truly senior cleared geospatial AI talent is rare, expensive as a full-time hire, and often unavailable on the timelines task orders demand.
BlueLens Analytics exists to solve that problem. A cleared small business with a single principal analyst who brings Project Maven credentials, 30 years of domain expertise, and production ML engineering capability — available for subcontracting and teaming without the overhead of a permanent hire, the delays of recruiting, or the risk of onboarding someone who hasn't actually done this work in operational environments.
The geographic footprint helps too. BlueLens Analytics operates from the NC Sandhills — Moore County, within the Fort Liberty / SOCOM corridor. JGASS II work happens in North Carolina. That's not a coincidence.
Project Maven launched in 2017 as the Department of Defense's primary initiative to apply machine learning to satellite imagery and full-motion video — closing the processing gap as ISR data volumes outpaced human analyst capacity. Chris Coffey served as a Principal Lead operating from within NGA as the primary GEOINT intelligence partner for the program.
The core work: developing and operationally deploying computer vision algorithms on satellite imagery and multi-sensor data streams, with emphasis on improving detection accuracy and reducing false positives to thresholds reliable enough for live mission use. Close collaboration across DoD components, combatant commands, and the broader IC — operating under explicit mandate to move at commercial AI development speed rather than traditional defense acquisition timelines.
The production systems built for Maven — automated change detection, multi-sensor fusion, and high-throughput ML pipelines — directly underpin the methodologies BlueLens Analytics brings to subcontracting engagements today.
From 2011 to 2022, Chris served as a Senior Geospatial Intelligence Analyst and Software Developer at the National Geospatial-Intelligence Agency. His NGA career included NGA Targeting, embedded deployment as AFRICOM J2 at RAF Molesworth in the UK, and embedded technical support to JSOC and Tier 1 special operations units in Central Africa.
He trained at CIA's training facility and Academi. He holds a TS/SCI clearance with full scope polygraph — Active.
As a technical leader at NGA, he led development teams building enterprise GEOINT platforms serving 200+ concurrent users across high-security environments — delivered six months ahead of a two-year schedule. He built Python automation frameworks reducing data preparation time by 60% and increasing production velocity by 250%. He was a Certified Instructor at the National Geospatial-Intelligence College teaching ML, Python, remote sensing, and database design.
Every item below has been used in production operational environments, not laboratory or research settings.
Deployed NASA-IBM Prithvi ViT foundation model for semantic segmentation of burn scars across multiple geographic regions and vegetation types. 96% overall accuracy, 73% IoU on 30m multispectral HLS data. Comparative evaluation of U-Net, SegFormer, and ViT demonstrated superior transformer performance. Validated against traditional spectral index methods (NBR).
Comprehensive methodology for detecting and classifying infrastructure damage using SAR coherence, optical, and thermal fusion. 85%+ classification accuracy across 10,000+ structures. 90% reduction in manual analysis time versus traditional methods. Evidence documentation protocols adopted by humanitarian monitoring organizations.
Cloud-based automated acquisition, preprocessing, and analysis pipeline ingesting from NASA Earthdata, ESA Copernicus, and commercial providers. 500+ GB daily processing. Event detection latency reduced from 48 hours to under 6 hours via incremental updates and parallel processing on GCP.