Weather Scientist (Numerical Weather Prediction)

• Build and benchmark next-generation multiscale, regional, and global forecasting systems against reanalysis and observations, with particular focus on nowcasting and extreme events. The work rests on careful treatment of station, radar, satellite, and other observational data, and on geospatial alignment to model grids.
• Build and operate a cycling data assimilation pipeline for our operational forecasting models, and produce the high-resolution gridded products it enables downstream.
• Develop observation quality control, bias correction (VarBC), and thinning workflows that hold up at operational data volumes and degrade gracefully when feeds drop out.
• Choose, deploy, and adapt a modern DA framework (e.g. JEDI/UFO, GSI, DART, PDAF) for our regional and global needs.
• Run cycling DA–forecast loops end to end lateral boundary conditions, SSTs, soil states, and spin-up at convection-permitting (~1 km) resolution over Indian sub-regions.
• Stand up rigorous forecast verification across deterministic (RMSE, bias, spectra) and probabilistic (CRPS, BSS) metrics.
• Tailor weather prediction models to renewable-sector needs, particularly solar (GHI) and wind generation (100m winds).
• Assist in training AI-based weather prediction models.
• Work at the intersection of physics-based modeling and machine learning hybrid physics–ML systems, learned parameterizations, and emulators.

• 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 is also acceptable.
• Hands-on work with limited-area or mesoscale models such as WRF, MPAS, or comparable systems including dynamical cores, physics parameterizations, and boundary-layer/convection schemes configuring and running them end to end (domains, lateral boundaries, physics suites, spin-up and stability), tuning parameterizations, diagnosing systematic biases, and verifying against observations or reanalysis.
• Experience running convection-resolving simulations at high spatial resolution (~1 km).
• Demonstrated depth in data assimilation, evidenced by operational work, model contributions, research projects, publications, or technical reports.
• Hands-on experience across the DA toolkit: observation operators and error specification; variational (3D-/4D-Var) or ensemble (EnKF, LETKF, EDA) methods; cycling workflows and innovation statistics; and assimilation of satellite, radar, radiosonde, or station observations.
• Familiarity with existing operational forecasting models (IFS, GFS, BharatFS).
• Experience contributing to or maintaining model code, maintaining data assimilation pipelines or holding responsibility in an operational or quasi-operational forecasting pipeline.
• Experience working with TB-scale, high-dimensional observational and modeling datasets (reanalysis, satellite, radar, weather-station, and sounding data) and the geospatial pipework (grids, reprojection, masks) around them.
• Hands-on experience with widely used 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.
• Experience building reproducible, production-grade pipelines.
• Excellent written and verbal communication, including the ability to explain technical work to both domain experts and cross-disciplinary collaborators.

• Prior work on projects specific to Indian geography.
• Familiarity with coupled earth-system models.
• Experience with any of: 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.

• 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.

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 work in data-sparse regions where data is heterogeneous, ground truth is incomplete, and progress requires both technical depth and first-principles thinking.

Working hours The team is distributed across India and the US, so expect a few hours of evening overlap with US Pacific Time and IST on most workdays.

Pravah is building the world's first foundation model of the electric grid. The world's most important physical industry is under unprecedented stress. We are using ML to build the 22nd-century grid. We are backed by Khosla Ventures, Pear VC, and Conviction.

We work directly with state-owned distribution and transmission electricity utilities to transform their decades of fragmented operational data into deployable, decision-grade intelligence. Our customers include some of the largest DISCOMs in the country, and our work spans demand forecasting (including renewable generation forecasting), weather forecasting, network mapping, and load flow analysis.