Key Responsibilities and Required Skills for Infrared Analyst

🎯 Role Definition

As an Infrared Analyst, you will apply advanced thermal and infrared imaging science, radiometric correction, and geospatial analysis to extract actionable insights from airborne, satellite, drone, and ground-based IR sensors. You will design and execute processing pipelines, validate sensor performance, develop anomaly detection and target-characterization algorithms, and translate technical outputs into clear, mission-relevant products for scientists, engineers, and operational users. The role requires strong hands-on experience in thermal image processing, atmospheric and emissivity correction, remote sensing tools (ENVI, ArcGIS/QGIS), coding in Python/MATLAB, and applied machine learning for imagery.

📈 Career Progression

Typical Career Path

Entry Point From:

Remote Sensing Technician or Geospatial Technician
Optical/Thermal Sensor Test Engineer
Image Processing Analyst or GIS Analyst

Advancement To:

Senior Infrared/Remote Sensing Scientist
Principal Sensor Scientist or Payload Lead
Manager, Remote Sensing & Analytics / Product Lead

Lateral Moves:

Computer Vision / Machine Learning Engineer (imagery focus)
Geospatial Data Scientist
Sensor Systems Integration Engineer

Core Responsibilities

Primary Functions

Lead the end-to-end processing, calibration, and quality assurance of thermal infrared and multispectral/hyperspectral imagery from multiple platforms (satellite, airborne, UAV, handheld), producing radiometrically and geometrically corrected datasets suitable for analysis and decision-making.
Design, implement, and maintain robust radiometric calibration workflows, including dark current subtraction, flat field correction, responsivity adjustment, and absolute radiance conversion to ensure sensor outputs are traceable and comparable across deployments.
Develop and apply atmospheric correction and emissivity estimation algorithms to derive accurate surface temperature, thermal anomaly, and material property products in both clear-sky and partially cloudy environments.
Perform advanced thermal image processing tasks such as noise reduction, destriping, vignetting correction, mosaicking, orthorectification, and sub-pixel registration to improve spatial and temporal consistency in imagery.
Create and validate automated anomaly detection and change detection pipelines for thermal events, hot-spot identification, energy loss mapping, and time-series thermal trend analysis using statistical and machine learning techniques.
Integrate geospatial datasets (DEM/DTM, land cover, cadastral boundaries, LiDAR) with IR imagery to perform contextual analysis, derive mapped products, and produce georeferenced deliverables compatible with GIS systems.
Prototype and productionize computer vision and deep learning models (e.g., CNNs, object detectors, semantic segmentation) for feature extraction and object classification in thermal and multispectral imagery, including model evaluation and bias assessment.
Conduct sensor characterization and performance assessment in lab and field environments, including MTF, NETD, SNR measurements, spectral response mapping, and cross-calibration against reference instruments and standards.
Plan, coordinate, and execute field data collections with thermal sensors and reference instrumentation (blackbody calibration sources, ground truth temperature probes), and document field calibration and validation procedures for traceability.
Work closely with sensor hardware, payload, and systems engineers to define requirements, specify sensor calibration needs, and integrate firmware/software updates that affect radiometric performance or metadata generation.
Author and maintain standard operating procedures (SOPs), data processing pipelines, metadata standards, and quality control checklists to ensure reproducible, auditable, and scalable IR processing operations.
Produce technical reports, thermal maps, sensor performance reports, and concise executive summaries that translate complex infrared analysis into mission-relevant findings and actionable recommendations for non-technical stakeholders.
Collaborate with cross-functional teams (data scientists, mission planners, operators, subject matter experts) to tailor IR analytics for applications such as infrastructure inspection, environmental monitoring, search & rescue, defense and intelligence, industrial process monitoring, and scientific research.
Evaluate new algorithms, open-source libraries, and commercial tools for IR analytics (e.g., ENVI, ERDAS, OpenCV, scikit-image) and recommend adoption strategies that balance accuracy, performance, and operational constraints.
Design and run synthetic and real-data experiments to validate algorithm robustness across a range of environmental conditions, sensor types, and deployment scenarios; document failure modes and mitigation approaches.
Implement scalable, reproducible processing pipelines using scripting, containerization (Docker), and workflow orchestration to support regular production runs and on-demand analyses.
Manage and curate large thermal imagery archives, ensuring correct metadata tagging (geolocation, time-stamp, sensor calibration parameters), versioning, and efficient retrieval for operational and research use.
Support end-to-end product delivery including web map services, GIS-ready layers, GeoTIFF exports, and interactive dashboards to enable stakeholders to explore thermal datasets and derived analytics.
Provide technical mentorship and training to junior analysts, field technicians, and end users on best practices for thermal data collection, processing, and interpretation.
Ensure compliance with data security, export control, and privacy policies when handling thermal imagery of sensitive areas or subjects, and coordinate with security teams as necessary.
Lead root-cause analysis on anomalous sensor outputs or processing failures, coordinate corrective action with hardware vendors, and implement process improvements to reduce recurrence.
Maintain awareness of advances in IR sensor technology, radiometry, atmospheric modeling, and AI-driven image analytics; present research findings and operational improvements to internal teams and at conferences or client briefings.
Support integration of IR-derived products into client workflows and decision-support systems, validate integration performance, and iterate based on user feedback and operational metrics.
Participate in pre-mission planning, defining sensor selection, flight lines, acquisition windows, and calibration strategies to maximize data quality and mission effectiveness under constraints (e.g., weather, airspace, logistics).

Secondary Functions

Support ad-hoc data requests and exploratory data analysis for stakeholders, rapidly delivering prototype thermal analytics and visualizations to inform time-sensitive decisions.
Contribute to the organization's data strategy and roadmap by identifying gaps in thermal data coverage, proposing new sensor acquisitions, and recommending processing automation priorities.
Collaborate with business units to translate data needs into engineering requirements for IR products, including accuracy requirements, deliverable formats, and update cadences.
Participate in sprint planning and agile ceremonies within the data engineering and analytics teams to prioritize IR tasks, define acceptance criteria, and drive measurable deliverables.

Required Skills & Competencies

Hard Skills (Technical)

Thermal / infrared image processing and radiometric correction (radiance-to-temperature conversion, emissivity modeling, NETD handling).
Atmospheric correction techniques for thermal bands (MODTRAN, 6S, or custom radiative transfer adjustments).
Experience with remote sensing software: ENVI, ERDAS Imagine, QGIS, ArcGIS Pro, and geospatial libraries (GDAL, rasterio).
Proficiency in Python for remote sensing and image analysis (NumPy, SciPy, OpenCV, scikit-image, rasterio, xarray).
MATLAB or IDL experience for legacy processing scripts and algorithm prototyping.
Hands-on knowledge of hyperspectral and multispectral analysis principles, band math, and spectral unmixing.
Machine learning and deep learning for imagery (scikit-learn, TensorFlow, PyTorch), including model training, validation, and deployment.
Sensor characterization and calibration experience (MTF, SNR, spectral response, blackbody calibration).
Geospatial data integration and GIS analysis, including coordinate reference systems, orthorectification, and DEM usage.
UAV/drone thermal systems operation and data workflows, including mission planning and field calibration techniques.
Proven ability to build automated processing pipelines, containerized workflows (Docker), and CI/CD practices for geospatial analytics.
Familiarity with data formats and APIs: GeoTIFF, NetCDF, HDF5, OGC services (WMS, WCS), and cloud storage (AWS S3, Google Cloud Storage).
Strong scripting and data manipulation skills (bash/shell, SQL) and experience with Linux/Unix environments.
Experience with version control (Git) and reproducible research/data provenance practices.
Data visualization and reporting tools: Matplotlib, Seaborn, Plotly, Tableau, or Power BI for communicating thermal findings.

Soft Skills

Strong analytical and problem-solving mindset with attention to detail when validating sensor outputs and algorithmic results.
Excellent written and verbal communication skills for producing clear technical reports, summaries, and client-facing deliverables.
Collaborative team player who can work with engineers, scientists, field teams, and stakeholders to translate requirements into solutions.
Ability to prioritize competing tasks in fast-paced, mission-driven environments and deliver high-quality results under schedule pressure.
Self-motivated learner who stays current with remote sensing literature, sensor advances, and machine learning techniques.

Education & Experience

Educational Background

Minimum Education:

Bachelor’s degree in Remote Sensing, Geospatial Science, Electrical Engineering, Physics, Atmospheric Science, Computer Science, or a related technical field.

Preferred Education:

Master’s or PhD in Remote Sensing, Applied Physics, Electrical/Optical Engineering, Computer Vision, or Geospatial Data Science with emphasis on infrared/thermal systems.

Relevant Fields of Study:

Remote Sensing / Earth Observation
Electrical, Optical, or Aerospace Engineering
Computer Science / Machine Learning
Atmospheric Science / Radiative Transfer
Geospatial Science / GIS

Experience Requirements

Typical Experience Range: 3–8+ years working with infrared/thermal imaging, radiometric processing, or remote sensing analytics.

Preferred:

5+ years of progressive experience in thermal image analysis, sensor calibration, or related remote sensing roles.
Demonstrated track record of delivering end-to-end IR products, operationalizing processing pipelines, and integrating imagery into decision support systems.
Experience in field calibration campaigns and practical use of reference instrumentation for ground-truthing.