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Quantitative Meteorologist Jobs (NOW HIRING)

Jane Street

Senior Weather Analyst/ML Researcher

New York, NY · On-site

$126K - $127K/yr

... meteorology, renewable energy forecasting * Experience using gridded data, observational data and satellite data to drive quantitative research in academia or industry * Experience with AI models ...

Hanchey

Senior Air Quality Specialist

Los Angeles, CA · On-site

$400K/yr

Solve quantitative and qualitative problems * Conduct presentations on technical topics for clients ... Meteorology, Chemistry, Physics, or related subjects * Minimum of seven years of experience ...

Gnostech

Software Engineer III

San Diego, CA · On-site

$125K - $175K/yr

... Pacific) Meteorology and Oceanography (METOC) In-Service Engineering (ISEA) Programs. This role ... Master's degree in a quantitative field such as engineering or mathematics (e.g. Electrical ...

Lockheed Martin

Metrologist (Level 2)

Palmdale, CA · On-site

... quantitative terms; developing and evaluating calibration systems that measure characteristics of objects, substances, or phenomena, such as length, mass, time, temperature, electric current ...

Gnostech LLC

Software Engineer III

San Diego, CA · On-site +1

$125K - $175K/yr

... Pacific) Meteorology and Oceanography (METOC) In-Service Engineering (ISEA) Programs. This role ... Master's degree in a quantitative field such as engineering or mathematics (e.g. Electrical ...

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How much do quantitative meteorologist jobs pay per year?

As of Jun 7, 2026, the average yearly pay for quantitative meteorologist in the United States is $96,278.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $98,500.00 per year, depending on experience, location, and employer.

What is the difference between Quantitative Meteorologist vs Climate Data Analyst?

AspectQuantitative MeteorologistClimate Data Analyst
Required CredentialsBachelor's or Master's in Meteorology, Atmospheric Science, or related field; often certifications in meteorologyBachelor's or Master's in Environmental Science, Climatology, or related field; data analysis skills
Work EnvironmentWeather stations, research labs, government agencies, mediaResearch institutions, government agencies, environmental organizations
Employer & Industry UsageWeather forecasting, aviation, agriculture, mediaClimate research, policy analysis, environmental consulting

While both roles involve analyzing atmospheric data, Quantitative Meteorologists focus on weather prediction and short-term forecasting using statistical models, whereas Climate Data Analysts study long-term climate patterns and trends. The roles share similar educational backgrounds and work environments but differ in their primary focus and application.

What are the key skills and qualifications needed to thrive as a Quantitative Meteorologist, and why are they important?

To thrive as a Quantitative Meteorologist, you need a strong background in atmospheric science, advanced mathematics, and statistical analysis, often supported by a degree in meteorology or a related field. Proficiency with numerical weather prediction models, programming languages like Python or Fortran, and data visualization tools is typically required. Excellent problem-solving skills, attention to detail, and effective communication are key soft skills for interpreting data and presenting findings to diverse audiences. These skills are crucial for producing accurate forecasts, advancing research, and informing critical weather-related decisions.

What is a Quantitative Meteorologist?

A Quantitative Meteorologist is a scientist who uses mathematical models, statistical techniques, and computational tools to analyze atmospheric data and forecast weather patterns. They focus on quantifying weather phenomena, such as precipitation, temperature, and wind speed, to provide accurate predictions and assessments. Quantitative Meteorologists often work with large datasets, develop weather models, and contribute to research that improves forecasting accuracy. Their work supports sectors such as agriculture, aviation, energy, and emergency management by providing data-driven insights for decision-making.

How do Quantitative Meteorologists typically collaborate with other teams to improve weather forecasting models?

Quantitative Meteorologists often work closely with software engineers, data scientists, and operational meteorologists to refine and implement advanced weather prediction models. Collaboration usually involves regular meetings to discuss model performance, integrating new data sources, and addressing computational challenges. This cross-functional teamwork ensures that forecasts are both scientifically robust and practically useful for stakeholders such as emergency managers or the public. Effective communication and a willingness to learn from diverse perspectives are key to success in this collaborative environment.
Infographic showing various Quantitative Meteorologist job openings in the United States as of May 2026, with employment types broken down into 6% Internship, and 94% Full Time. Highlights an 70% In-person, 6% Hybrid, and 24% Remote job distribution, with an average salary of $96,278 per year, or $46.3 per hour.

Satellite Applications Specialist - Precipitation

Rainmaker Technology Corporation

El Segundo, CA • Remote

Full-time

Posted 24 days ago

Job description

Rainmaker is pioneering a modern cloud seeding system to solve water scarcity and inclement weather problems. We develop and incorporate radar validation, weather-resistant UAS, numerical weather modeling, and sustainable cloud seeds into an effective precipitation enhancement solution.

The Satellite Applications Specialist is responsible for the acquisition, processing, and analysis of satellite cloud and precipitation data for cloud seeding validation. This role requires a deep understanding of satellite remote sensing principles, meteorological processes, and data analysis techniques. The specialist will work closely with meteorologists, data scientists, and software engineers to develop and implement advanced data processing algorithms and workflows.
What You'll Do
  • Data Acquisition: Identify and access publicly available meteorological satellite data from various platforms (e.g., GOES, Meteosat, MODIS, Sentinel, etc.). Develop and maintain data acquisition workflows to retrieve large multi-spectral datasets. Implement data storage and management strategies.
  • Data Ingestion and Processing: Ingest satellite data into data processing systems. Perform data quality checks. Define and implement correction methodologies. Apply atmospheric correction algorithms to account for the effects of the atmosphere on satellite measurements. Develop and implement advanced algorithms for quantitative precipitation estimation (QPE) and phase classification based on machine learning methods.
  • Data Analysis: Analyze satellite data to extract meteorological information, such as cloud cover, precipitation intensity, cloud phase, and atmospheric temperature and humidity profiles. Work to analyze real-time and archival case studies. Develop and apply data visualization techniques to represent meteorological phenomena. Contribute to scientific research projects and publish findings in peer-reviewed journals.
  • Algorithm Development: Develop and test new algorithms for satellite data processing and analysis. Collaborate with data scientists and meteorologists to improve existing algorithms. Stay up-to-date on the latest advancements in satellite remote sensing and data analysis techniques.
  • Precipitation Enhancement Applications: Support precipitation enhancement operations by providing satellite data analysis to identify suitable cloud targets and assess the effectiveness of seeding efforts. Develop algorithms to estimate the potential for precipitation enhancement from precipitation enhancement.
Required Qualifications
  • Master's degree or Ph.D. in meteorology, atmospheric science, remote sensing, or a related field.
  • Strong understanding of satellite remote sensing principles and meteorological processes.
  • Experience with data acquisition, processing, and analysis of large datasets. This involves distributed computing methods, database management systems, data partitioning, vectorizing operations, and GPU acceleration.
  • Experience with Amazon Web Services cloud computing for external data sources, and internal instance management.
  • Proficiency in data analysis tools and programming languages (e.g., Python, MATLAB, R, C++).Knowledge of machine learning and deep learning techniques, for satellite applications.
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