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Seasonal Weather Forecasting Jobs (NOW HIRING)

Pravāh

AI Weather Scientist

San Francisco, CA · On-site

$150K - $250K/yr

Inform the development of AI weather forecasting models and innovate on existing architectures ... seasonal outlooks, crop-relevant variables), and extreme-weather resilience (heatwaves, heavy ...

Portland General Electric

Meteorologist

Tualatin, OR · On-site

The candidate must have expertise in forecasting critical fire weather patterns that lead to high fire danger. The successful candidate will also produce seasonal weather outlooks, lead or support ...

Zipline

Head of Weather Systems

South San Francisco, CA

$235K - $255K/yr

... forecast in-coming weather conditions that would require pausing deliveries; * Define the ... both seasonal and daily timescales * Drive the development of ground- and air-based sensing to ...

Knoebels

Comm Center Team Member

Elysburg, PA

$13.50 - $17/hr

This is a seasonal position, reporting to the Comm Center Manager. Park's operational summer hours ... Check weather forecast to keep team members and guests informed and safe KEY COMPETENCIES

Knoebels

Comm Center Team Member

Elysburg, PA · On-site

$13.50 - $17/hr

This is a seasonal position, reporting to the Comm Center Manager. Park's operational summer hours ... Check weather forecast to keep team members and guests informed and safe KEY COMPETENCIES

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Seasonal Weather Forecasting information

What are the key skills and qualifications needed to thrive as a Seasonal Weather Forecaster, and why are they important?

To thrive as a Seasonal Weather Forecaster, you need strong expertise in meteorology, climate science, and data analysis, often supported by a degree in atmospheric sciences or a related field. Familiarity with forecasting models, statistical analysis software, and Geographic Information Systems (GIS) is typically required. Excellent problem-solving, communication, and teamwork skills help convey complex forecasts to diverse audiences and collaborate with stakeholders. These combined abilities ensure accurate, actionable seasonal forecasts that support decision-making in weather-sensitive sectors.

What is seasonal weather forecasting?

Seasonal weather forecasting is the process of predicting average weather conditions—such as temperature and rainfall—over a period of several weeks to several months, typically up to a season ahead. Unlike short-term forecasts that predict specific daily weather, seasonal forecasts provide general trends and probabilities for broader timeframes. These forecasts use climate models, historical data, and current atmospheric and oceanic conditions (like El Niño or La Niña) to make predictions. Seasonal forecasts are valuable for agriculture, energy planning, water resource management, and disaster preparedness.

What is the difference between Seasonal Weather Forecasting vs Meteorologist?

AspectSeasonal Weather ForecastingMeteorologist
CredentialsDegree in meteorology or atmospheric sciences, certifications varyDegree in meteorology, atmospheric sciences, or related field; often includes certifications
Work EnvironmentResearch centers, government agencies, climate organizationsTV stations, radio, government agencies, private firms
Industry UsageFocuses on long-term seasonal predictionsProvides daily weather updates, forecasts, and analysis
Search & Comparison IntentUnderstanding seasonal climate patternsDaily weather forecasts and analysis

Seasonal Weather Forecasting specializes in predicting climate trends over months, aiding agriculture and planning. Meteorologists provide daily weather updates and short-term forecasts. While both roles require meteorological knowledge, seasonal forecasting emphasizes long-term climate patterns, whereas meteorologists focus on immediate weather conditions.

What are some common challenges faced in a seasonal weather forecasting role, and how can they be managed?

Professionals in seasonal weather forecasting often encounter challenges such as limited historical data, high model uncertainty, and rapidly changing climate patterns. Managing these challenges involves leveraging advanced statistical models, continuously updating data inputs, and collaborating closely with climatologists and data scientists. It's important to communicate forecast uncertainty clearly to stakeholders and stay current with the latest research and technological advancements in the field. Teamwork and ongoing professional development are key to overcoming these obstacles and delivering accurate, actionable forecasts.
What cities are hiring for Seasonal Weather Forecasting jobs? Cities with the most Seasonal Weather Forecasting job openings:
What are the most commonly searched types of Weather Forecasting jobs? The most popular types of Weather Forecasting jobs are:
What states have the most Seasonal Weather Forecasting jobs? States with the most job openings for Seasonal Weather Forecasting jobs include:
Seasonal Weather Forecasting jobs near you

AI Weather Scientist

Pravāh

San Francisco, CA • On-site

$150K - $250K/yr

Full-time

Posted 17 days ago

Job description

About Pravāh
Pravah is building foundational intelligence for the electric grid. We apply modern machine learning to complex physical infrastructure problems spanning grid operations, weather, and geospatial systems.
Our work sits at the intersection of computer vision, physical systems, and large-scale ML, with deployments across utilities in the United States and India. We leverage multimodal data - including satellite imagery, LiDAR, and street-level data - to build high-fidelity representations of grid assets and their surroundings.
We are backed by Khosla Ventures, Pear VC, and Conviction.
To know more about who we are, what we are building, and why we are excited read this Notion! https://pravah.notion.site/
The role
We are hiring an AI Weather Scientist to advance the next generation of weather forecasting systems. You will work closely with machine learning and software engineers on four core threads:
  • Run numerical weather prediction models to generate high-resolution forecasts and training data.
  • Inform the development of AI weather forecasting models and innovate on existing architectures.
  • Evaluate pre-trained global and regional models against reanalysis, satellite, and ground observations to identify areas for improvement.
  • Procure, process, and create ML-ready global and regional weather datasets, with explicit focus on data-sparse regions.

What you'll work on
  • Drive the development of next-generation multiscale, regional, and global weather forecasting systems, and their benchmarking against reanalysis and observations, especially during extreme events and over data-sparse regions.
  • Tailor weather prediction models to sector-specific needs: energy (solar and wind demand/generation, grid stress), agriculture (seasonal outlooks, crop-relevant variables), and extreme-weather resilience (heatwaves, heavy precipitation, tropical and extratropical cyclones, convective storms).
  • Assess the applicability of state-of-the-art AI methodologies including foundation models, generative architectures, and physics-informed ML to weather and climate forecasting.
  • Work at the intersection of physics-based modeling and machine learning: hybrid physics-ML approaches, learned parameterizations, and emulators.

Who you are
Any combination of the following will strengthen your application. We do not expect you to have all of them.
Preferred qualifications*
  • A master's or PhD in geophysical sciences, physics, applied mathematics, computer science, statistics, or a related field. A bachelor's with 7+ years of relevant research or operational experience is also acceptable.
  • Demonstrated depth in either numerical weather prediction, meteorology, or earth system modeling through research projects, publications, model contributions, or operational work.
  • Experience working with high-dimensional observational and modeling datasets (reanalysis products, satellites, weather stations) in forecasting
  • Experience working with deep learning models and familiarity with at least one framework (PyTorch, JAX, or TensorFlow).*
  • Excellent written and verbal communication, including the ability to explain technical work to both domain experts and cross-disciplinary collaborators.

Nice-to-have
  • Hands-on experience with high-resolution regional earth-system models such as WRF or MPAS, including dynamical cores, physics parameterizations, boundary-layer and convection schemes, or coupled ocean-atmosphere configurations.
  • Experience with operational forecasting models or workflows (real-time data ingest, verification, cycling, product generation).
  • Experience with either of data assimilation, ensemble and probabilistic forecasting, convection-permitting or mesoscale modeling, regional downscaling, or subseasonal-to-seasonal (S2S) prediction.
  • Experience using or building AI weather prediction models - whether benchmarking, fine-tuning, or extending them. Applying generative AI and diffusion models to weather and climate is a strong plus.
  • Publications in leading atmospheric, oceanic, or climate science venues and/or major ML/AI conferences.

What you'll gain
  • Ownership of weather forecasting models deployed for real-time applications.
  • Experience working on hard, open-ended problems at the intersection of AI and physical infrastructure.
  • Ability to shape technical direction and shape the frontier of AI-weather prediction revolution.
  • Close collaboration with a deeply technical founding team.
Why this role
This role sits at the frontier of the AI-weather revolution, applying modern machine learning to earth system modeling. The next decade of progress in weather and climate prediction will be built by scientists who understand the physics and the data and have learned to wield generative AI. You will be working in data-sparse regions where data is heterogeneous, ground truth is incomplete, and progress requires both technical depth and first-principles thinking.

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