Atmospheric Scientist

• Opportunity for advancement
• Training & development
• 401(k)
• 401(k) matching
• Bonus based on performance
• Competitive salary
• Flexible schedule

PEMDAS® Technologies & Innovations is seeking an Atmospheric Scientist to support applied research and development across weather analysis, forecasting, remote sensing, and decision-support tools. The ideal candidate combines strong scientific knowledge with programming and data-analysis skills, and can turn complex atmospheric data into practical insights for operational users.

• Analyze atmospheric and environmental data from radar, satellites, surface observations, soundings, and numerical weather prediction models.
• Develop and evaluate methods, algorithms, and tools for forecasting, now casting, and severe-weather analysis.
• Use Python and other scientific programming languages to process, visualize, and interpret large datasets.
• Apply statistical methods and machine learning to improve forecasts, diagnostics, and decision-support capabilities.
• Support model evaluation, validation, and verification using observational data.
• Collaborate with scientists, engineers, and operational partners on applied research and research-to-operations efforts.
• Present findings through technical reports, briefings, and clear documentation for both technical and nontechnical audiences.
• Contribute to project planning and deliver high-quality work on schedule.

• Bachelor's degree in meteorology, atmospheric science, or a closely related field; a master's degree or Ph.D. is preferred.
• Experience analyzing atmospheric data and interpreting weather systems.
• Proficiency in Python or another scientific programming language.
• Experience with numerical weather prediction, remote sensing, radar, satellite, or observational datasets.
• Strong written and verbal communication skills.
• Ability to work independently and collaboratively in a remote, multidisciplinary team.

• Graduate-level education or equivalent applied research experience in atmospheric science or meteorology.
• Experience with machine learning, statistical analysis, or scientific algorithm development.
• Familiarity with tools such as xarray, Num Py, pandas, scikit-learn, PyTorch, ArcGIS, or QGIS.
• Experience with Linux, cloud-based data, high-performance computing, or operational forecasting environments.
• Peer-reviewed publications, conference presentations, or experience translating research into operational applications.

Remote

Competitive and based on experience

This is a remote position.