Senior Lightning Scientist

Role overview

Advance the science and engineering behind lightning detection and interpretation. This role combines atmospheric electricity research, radio-frequency analysis, and algorithm development to improve real-time and historical lightning data, while helping teams turn scientific findings into dependable operational tools.

Responsibilities - Lead the design and evaluation of lightning detection and location methods, including time-based, interferometric, or hybrid approaches. - Analyze radio signals, characterize waveforms, reduce noise, and develop signal processing for real-time and post-processed observations. - Work with hardware specialists on sensor and antenna choices, placement, and calibration. - Validate location, flash grouping, event classification, and data quality against ground truth and statistical measures. - Connect lightning observations with forecasting, severe-weather analysis, and machine learning workflows. - Guide colleagues, document methods and limitations, and explain findings to technical and nontechnical audiences.

Requirements - Advanced degree in atmospheric science, electrical engineering, physics, geophysics, or a related field; a master’s degree paired with substantial experience is acceptable. - Strong understanding of lightning physics, electromagnetic emissions, and storm electrification. - Experience with RF systems, signal processing, spectral analysis, and lightning detection or geolocation methods. - Ability to develop algorithms and analyze data using Python, C++, or comparable scientific tools. - Experience working with large-scale, real-time observational data and communicating complex technical concepts clearly.

Nice to have - Experience with lightning sensors or ground-based detection networks, antenna design, or RF propagation. - Familiarity with FPGA, software-defined radio, embedded acquisition, cloud data platforms, or high-throughput pipelines. - Research publications or patents in lightning detection, RF sensing, or atmospheric electricity; experience with forecasting teams or machine learning for signal classification.