Weather Scientist (Numerical Weather Prediction)

The role

You will advance next-generation weather forecasting systems for India using numerical weather prediction and data assimilation. You will build forecasting systems, operate data assimilation pipelines, and create ML-ready weather datasets.

What you will do

• Build and benchmark multiscale, regional, and global forecasting systems.
• Build and operate a cycling data assimilation pipeline.
• Develop observation quality control, bias correction, and thinning workflows.
• Choose, deploy, and adapt a modern DA framework.
• Run cycling DA–forecast loops end to end.
• Stand up rigorous forecast verification across deterministic and probabilistic metrics.
• Tailor weather prediction models to renewable-sector needs.
• Assist in training AI-based weather prediction models.
• Work at the intersection of physics-based modeling and machine learning hybrid systems.

Required qualifications

• A master's or PhD in geophysical sciences, physics, applied mathematics, computer science, statistics, or a related field.
• A bachelor's degree with 3+ years of relevant research or operational experience.
• Hands-on work with limited-area or mesoscale models such as WRF, MPAS, or comparable systems.
• Experience running convection-resolving simulations at high spatial resolution (~1 km).
• Demonstrated depth in data assimilation.
• Hands-on experience across the DA toolkit.
• Familiarity with existing operational forecasting models (IFS, GFS, BharatFS).
• Experience contributing to or maintaining model code or data assimilation pipelines.
• Experience working with TB-scale, high-dimensional observational and modeling datasets.
• Hands-on experience with reference datasets such as ERA5, MERRA-2, IMDAA, IMERG/GPM, and GOES/INSAT/Himawari.
• Practical experience on High Performance Computers (HPCs).
• Fluency in the modern geoscience Python stack: xarray, dask, zarr, netCDF.

Nice to have

• Prior work on projects specific to Indian geography.
• Familiarity with coupled earth-system models.
• Experience with ensemble and probabilistic forecasting, regional downscaling, or subseasonal-to-seasonal (S2S) prediction.
• Experience working with operational forecasting agencies (IMD, NCMRWF, ECMWF, NOAA, etc.).
• Familiarity with AI-based weather prediction models and data assimilation techniques.
• Comfort using agentic AI tools to accelerate development.
• Publications in respected atmospheric, oceanic, or climate science venues.

Benefits

• Part of development of weather forecasting models deployed for real-time applications.
• Experience working on hard, open-ended problems at the intersection of AI and physical infrastructure.
• Exposure to how teams set priorities and push the frontier of AI weather prediction.
• Close collaboration with a deeply technical team.

About the company

Pravah is building the world's first foundation model of the electric grid. They use ML to transform fragmented operational data into decision-grade intelligence for electricity utilities.