Remote Sensing Data Scientist & Program Lead

Urban Sky•Denver, CO

4h•$120,000 - $170,000•Onsite

About The Position

Urban Sky is seeking an experienced Remote Sensing Data Scientist to lead our NASA ROSES-funded "Hot Spot" project, a cutting-edge 3-year fire sensing program leveraging our stratospheric balloon platforms for wildfire detection and monitoring. This role combines deep technical expertise in remote sensing, algorithm development, and data analysis with program leadership responsibilities. As the primary technical and programmatic lead for Hot Spot, you will own the end-to-end development of Land Surface Temperature (LST) data products—from algorithm design and radiative transfer modeling through calibration, validation, and operational delivery. You will build and maintain data quality control pipelines, develop production-quality software, and translate instrument performance characteristics into robust retrieval algorithms. You will also manage the program schedule, maintain the customer relationship with NASA, and ensure successful execution of all project milestones. This position offers a unique opportunity to shape the future of stratospheric remote sensing while building relationships that will extend to other government agencies including the Air Force and Army. This is not a role for those seeking a well-trodden path—our work involves solving problems with no clear precedent, designing novel solutions, and iterating rapidly in ambiguous and fast-paced environments. You should be comfortable taking on big ambiguous projects at the forefront of remote sensing, data science, and earth observation. In this role you will have the autonomy to innovate at the cutting edge of stratospheric sensing technology.

Requirements

Bachelor’s or Master’s degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Physics, Engineering)
5+ years of experience in remote sensing, algorithm development, or related data science/engineering roles
Strong background in thermal infrared (TIR) sensing, Land Surface Temperature retrieval, and radiative transfer modeling
Proficiency in Python for production-quality algorithm development and data processing; C++ experience desirable
Experience building scalable data pipelines with emphasis on reproducibility, automated testing, and operational delivery
Strong understanding of calibration and validation methodologies for remote sensing instruments and derived data products
Experience with optical modeling tools (Zemax OpticStudio, Code V, FRED, or similar) and ability to translate instrument performance into algorithm design
Demonstrated experience managing technical programs or projects, preferably government contracts
Excellent written and verbal communication skills for technical documentation, publications, and customer engagement
Must be US Citizen or Permanent Resident
Must be located near Denver, CO or willing to relocate to Denver, CO

Nice To Haves

Experience with NASA ROSES or other NASA research programs
Background in wildfire detection, land surface temperature retrieval, or related earth science applications
Experience with atmospheric correction algorithms and sensitivity analysis
Experience building data QC systems including monitoring, anomaly detection, and reporting tools
Experience working with DoD customers (Air Force, Army, or other branches)
Familiarity with stratospheric or airborne remote sensing platforms
Track record of peer-reviewed publications in remote sensing or related fields

Responsibilities

Lead the NASA Hot Spot program as primary technical and programmatic owner, managing schedule, deliverables, and customer relationships
Lead algorithm development, calibration, and validation for Land Surface Temperature (LST) data products from thermal infrared (TIR) instruments, including uncertainty estimation and performance characterization
Implement atmospheric correction and radiative transfer modeling to support LST and secondary geophysical products; assess sensitivities to atmospheric state, surface emissivity, and instrument characteristics
Design, implement, and own the data quality control (QC) and validation pipeline for all data products, including defining quality metrics, thresholds, automated checks, monitoring, and anomaly detection
Maintain and extend the data processing codebase for operational delivery of scientific data products, emphasizing scalable workflows, reproducibility, automated testing, and clear documentation
Support optical and system-level modeling to inform both hardware development and data processing; perform optical performance analysis (e.g., MTF, SNR, stray light, distortion) using tools such as Zemax OpticStudio, Code V, or FRED
Translate instrument performance and optical effects into algorithm assumptions, correction strategies, and uncertainty models; collaborate with hardware teams on payload design, integration, testing, and performance trades
Support the design, development, testing, and calibration of high-performance EO payloads for imaging
Support mission design, trade studies, and requirement definition through data-driven analysis; work closely with instrument engineers and end users to ensure data products meet operational needs
Document and communicate results via technical reports, peer-reviewed publications, and presentations at internal forums and international conferences