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.<h2> Overview</h2><h3> About Pravāh</h3>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:<a target="_blank" rel="noopener noreferrer nofollow" href="https://www.pravah.com/"> Website</a>,<a target="_blank" rel="noopener noreferrer nofollow" href="https://pravah.notion.site/"> Pravāh on Notion</a>.<h2> The role</h2>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 three core threads:1. Data assimilation: contribute hands-on to data assimilation for weather forecasting models.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.<h2> What you'll work on</h2>· Build and operate a cycling data assimilation pipeline for our operational forecasting models, and produce the high-resolution gridded products it enables downstream.· Choose, deploy, and adapt a modern DA framework (e.g. JEDI/UFO, GSI, DART, PDAF) for our regional and global needs.· Develop observation quality control, bias correction (VarBC), and thinning workflows that hold up at operational data volumes and degrade gracefully when feeds drop out.· Contribute to AI-based data assimilation pipelines.· 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.<h2>Who you are</h2><h3>Required qualifications</h3>· 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 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.· Hands-on experience with at least one operational DA framework: JEDI/UFO, GSI, DART, PDAF, or an in-house equivalent, including building observation operators and forward models.· Working knowledge of bias correction (VarBC), adaptive QC, and gross-error rejection.· Experience contributing to or maintaining assimilation 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.<h3>Nice to have </h3>· 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.<h2> What you'll gain</h2>· 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.<h2> Why this role</h2>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.

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:<a target="_blank" rel="noopener noreferrer nofollow" href="https://www.pravah.com/"> Website</a>,<a target="_blank" rel="noopener noreferrer nofollow" href="https://pravah.notion.site/"> Pravāh on Notion</a>.

Weather Data Scientist: Data Assimilation

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.<h2> Overview</h2><h3> About Pravāh</h3>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:<a target="_blank" rel="noopener noreferrer nofollow" href="https://www.pravah.com/"> Website</a>,<a target="_blank" rel="noopener noreferrer nofollow" href="https://pravah.notion.site/"> Pravāh on Notion</a>.<h2> The role</h2>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 three core threads:1. Data assimilation: contribute hands-on to data assimilation for weather forecasting models.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.<h2> What you'll work on</h2>· Build and operate a cycling data assimilation pipeline for our operational forecasting models, and produce the high-resolution gridded products it enables downstream.· Choose, deploy, and adapt a modern DA framework (e.g. JEDI/UFO, GSI, DART, PDAF) for our regional and global needs.· Develop observation quality control, bias correction (VarBC), and thinning workflows that hold up at operational data volumes and degrade gracefully when feeds drop out.· Contribute to AI-based data assimilation pipelines.· 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.<h2>Who you are</h2><h3>Required qualifications</h3>· 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 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.· Hands-on experience with at least one operational DA framework: JEDI/UFO, GSI, DART, PDAF, or an in-house equivalent, including building observation operators and forward models.· Working knowledge of bias correction (VarBC), adaptive QC, and gross-error rejection.· Experience contributing to or maintaining assimilation 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.<h3>Nice to have </h3>· 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.<h2> What you'll gain</h2>· 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.<h2> Why this role</h2>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.

Weather Data Scientist: Data Assimilation

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Pravāh

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