Weather Data Scientist (Numerical Weather Prediction)

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

Pravāh is an AI lab 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 - some of the most ambitious investors in Silicon Valley.

More about who we are, what we are building, and why we are excited: Website, Pravāh on Notion.

We are hiring a Weather Data Scientist to advance the next generation of weather forecasting systems for India, with strong attention to observational data quality and geospatial consistency. You will work closely with machine learning and software engineers on two core threads:

1. Numerical weather prediction: run regional NWP models to generate high-resolution forecasts and training data.

2. ML-ready datasets: procure, process, and create ML-ready global and regional weather datasets at large scale (high volume, multi-source, long time horizons), with explicit focus on data-sparse regions.

· 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.

· 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.

· Demonstrated depth in numerical weather prediction, evidenced by operational work, model contributions, research projects, publications, or technical reports.

· 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).

· Familiarity with existing operational forecasting models (IFS, GFS, BharatFS).

· Experience contributing to or maintaining model code, 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.