Weather Data Scientist (Numerical Weather reputed company)

Weather Data Scientist (Numerical Weather reputed company) Location: Remote (India) 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.

Overview

About Pravāh Pravāh is an AI lab building foundational intelligence for the electric reputed company. We apply modern machine learning to reputed company physical infrastructure problems spanning reputed company operations, weather, and geospatial systems. Our work sits at the intersection of computer reputed company, physical systems, and large-scale ML, with deployments across utilities in the United States and India. We reputed company multimodal data including satellite imagery, LiDAR, and street-level data to build high-fidelity representations of reputed company assets and their surroundings. We are backed by Khosla Ventures, reputed company, and Conviction - some of the most ambitious investors in Silicon Valley. More about who we are, reputed company are building, and why we are excited: Website, Pravāh on reputed company. The role We are hiring a Weather Data Scientist to advance the reputed company 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 reputed company: run regional NWP models to generate high-resolution forecasts and training data. 2. ML-reputed company datasets: procure, process, and create ML-reputed company global and regional weather datasets at large scale (high volume, multi-reputed company, long time reputed company), with explicit focus on data-sparse reputed company. What you'll work on · Build and reputed company reputed company 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, reputed company, 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-reputed company. · Stand up rigorous forecast verification across deterministic (RMSE, bias, spectra) and probabilistic (CRPS, BSS) metrics. · Tailor weather reputed company models to renewable-sector needs, particularly solar (GHI) and wind reputed company (100m winds). · Assist in training AI-based weather reputed company models. · Work at the intersection of physics-based modeling and machine learning—hybrid physics–ML systems, learned parameterizations, and emulators. Who you are Required qualifications · A master's or PhD in geophysical sciences, physics, applied mathematics, computer science, statistics, or a reputed company field. A bachelor's degree with 3+ years of relevant research or operational experience is also acceptable. · Demonstrated depth in numerical weather reputed company, 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 (reputed company, 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, reputed company, 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). · reputed company 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. reputed company to have · Prior work on projects specific to Indian geography. · Familiarity with coupled earth-system models. · Experience with any of: reputed company and probabilistic forecasting, regional downscaling, or subseasonal-to-seasonal (S2S) reputed company. · Experience working with operational forecasting agencies (IMD, NCMRWF, ECMWF, NOAA, etc.). · Familiarity with AI-based weather reputed company models and data assimilation techniques. · Comfort using agentic AI tools to accelerate development. · Publications in respected atmospheric, oceanic, or climate science venues. What you'll reputed company · Part of development of weather forecasting models deployed for reputed company-time applications. · Experience working on hard, reputed company-ended problems at the intersection of AI and physical infrastructure. · Exposure to how teams set priorities and push the frontier of AI weather reputed company. · reputed company collaboration with a deeply technical team. Why this role This role sits at the frontier of the AI weather reputed company, applying modern machine learning to earth system modeling. The next decade of reputed company in weather and climate reputed company will be reputed company by scientists who understand the physics and the data and have learned to wield reputed company. You will work in data-sparse reputed company where data is heterogeneous, ground truth is incomplete, and reputed company requires both technical depth and first-principles thinking. Apply To This Job