{"schemaVersion":"jobsearcher.job.v1","id":"27e46e64f9d23c6fdf7b226b","url":"https://jobsearcher.com/jobs/27e46e64f9d23c6fdf7b226b","canonicalUrl":"https://jobsearcher.com/jobs/27e46e64f9d23c6fdf7b226b","title":"Quantitative Meteorologist","description":"About Rainmaker\nRainmaker is pioneering a modern cloud-seeding system to increase precipitation, improve water availability, and address severe-weather challenges. We combine atmospheric science, weather-resistant UAS, radar and satellite observations, numerical weather prediction, novel sensing systems, and sustainable seeding technologies to design, operate, and evaluate precipitation-enhancement programs.\n\nResearch at Rainmaker is attached directly to operations. Our scientists and engineers collect proprietary observations, deliberately intervene in atmospheric systems, evaluate the results, and use what they learn to improve the next operation.\n\nAbout the Role\nAs a Quantitative Meteorologist, you will bridge atmospheric science, statistical analysis, operational decision-making, and commercial program design.\n\nYou will develop rigorous methods for identifying when and where cloud-seeding operations are most likely to be effective, evaluating completed operations, improving real-time forecast and nowcast workflows, and assessing potential new programs. You will turn meteorological expertise that currently lives in individual judgment into repeatable analyses, decision systems, and defensible measures of performance.\n\nThis is not primarily a shift-forecasting role or a pure academic-research position. You will own ambiguous quantitative questions that span science, operations, product, and business development, and you will personally build the analyses and tools needed to answer them.\n\nWhat You'll Do\n\nDevelop quantitative methods for identifying, scoring, and ranking cloud-seeding opportunities.\n\nAnalyze historical and real-time meteorological data to understand the atmospheric and operational conditions associated with successful targeting and precipitation outcomes.\n\nDesign observational studies, experiments, and statistical analyses that distinguish intervention effects from natural weather variability as rigorously as the available data permits.\n\nEstablish honest uncertainty bounds and communicate when the evidence does not support a causal conclusion.\n\nBuild reusable tools for evaluating potential cloud-seeding programs, including climatology, seedable-hour frequency, targetability, operating constraints, expected opportunity, program design, and sensitivity analysis.\n\nWork with software engineers to automate meteorological forecasting and nowcasting workflows used by flight and field operations.\n\nDevelop decision-support methods that combine NWP, ensembles, radar, satellite, sounding, aircraft, UAS, surface, and in-situ observations.\n\nDefine ground truth, baselines, validation methods, and performance metrics for forecasting, retrieval, precipitation-estimation, and intervention-analysis systems.\n\nTranslate meteorological concepts into features, labels, physical constraints, evaluation frameworks, and failure cases for machine-learning work.\n\nWork with ML and software engineers on hybrid physical, statistical, and learning-based approaches while retaining responsibility for meteorological validity.\n\nProduce technical analyses that support customer proposals, program design, business development, scientific validation, and operational reviews.\n\nCreate stronger feedback loops between forecasting, field operations, sensor development, research, and model development.\n\nCommunicate results clearly to scientists, operators, engineers, customers, regulators, and nontechnical stakeholders.\n\nWhat We're Looking For\n\nAn advanced degree in meteorology, atmospheric science, applied mathematics, statistics, physics, or a related quantitative field, or equivalent evidence of exceptional quantitative meteorological ability.\n\nStrong understanding of cloud and precipitation processes, mesoscale meteorology, and numerical weather prediction.\n\nExperience applying statistical methods to noisy, spatially and temporally correlated environmental data.\n\nStrong Python and scientific-computing skills, including experience with tools such as NumPy, SciPy, pandas, xarray, and geospatial libraries.\n\nExperience working with meteorological data such as GRIB, netCDF, radar, satellite, model, sounding, aircraft, or surface observations.\n\nAbility to formulate ambiguous scientific and operational questions as measurable quantitative problems.\n\nExperience building reproducible analyses, automated workflows, datasets, or decision-support tools.\n\nStrong judgment about causality, confounding, uncertainty, validation, and the limits of observational evidence.\n\nClear written and verbal communication across scientific, operational, engineering, and commercial teams.\n\nHigh agency and willingness to do the analytical and implementation work personally.\n\nWe care deeply about demonstrated technical ownership. If you have a project, system, experiment, paper, portfolio, or technical write-up that shows how you approach difficult problems, include it with your application and tell us what you personally contributed.\n\nPreferred Qualifications\n\nA PhD in meteorology, atmospheric science, or a closely related field.\n\nExperience with cloud microphysics, orographic precipitation, convective precipitation, weather modification, hail, or field campaigns.\n\nExperience with WRF, HRRR, GFS, ECMWF products, data assimilation, ensembles, operational forecast verification, or meteorological post-processing.\n\nExperience with causal inference, experimental design, Bayesian methods, spatial statistics, time-series analysis, uncertainty quantification, or decision science.\n\nExperience developing statistical or ML models for weather, remote sensing, or physical systems.\n\nFamiliarity with radar meteorology, satellite retrievals, quantitative precipitation estimation, cloud-particle measurements, or atmospheric instrumentation.\n\nExperience designing or evaluating operational meteorological programs.\n\nExperience communicating quantitative results to customers, regulators, government agencies, or business-development teams.\n\nWhat Success Looks Like\n\nWithin your first year, you will have helped Rainmaker:\n\nQuantify and rank cloud-seeding opportunities more consistently.\n\nImprove the accuracy, speed, and automation of operational forecasting and nowcasting.\n\nEstablish repeatable and scientifically defensible methods for evaluating operational outcomes.\n\nIdentify changes to targeting or program design that can increase expected precipitation yield.\n\nEvaluate new regions and customer programs using rigorous meteorological and quantitative analysis.\n\nDefine better ground truth and evaluation frameworks for Rainmaker's ML, retrieval, and forecasting systems.\n\nCreate durable feedback loops between field operations, scientific research, commercial program design, and model development.\n\nBenefits\n\nSignificant stock options with high potential upside as an early-stage company\n\n401(k) with employer matching\n\nFull health coverage (medical, dental, and vision insurance)\n\nRelocation assistance provided (if applicable)\n\nUnlimited PTO\n\nPaid parental leave for both parents\n\nLunch provided when working in-office and a fully stocked kitchenette\n\nFree EV charging at the HQ\n\n#J-18808-Ljbffr","company":"Socket","rawCompany":"socket","city":"El Segundo","state":"CA","isRemote":false,"isActive":true,"createdAt":"2026-08-31T03:17:52.688Z","occupations":[{"code":"19-2021.00","title":"Atmospheric and Space Scientists","slug":"atmospheric-and-space-scientists"},{"code":"15-2041.00","title":"Statisticians","slug":"statisticians"},{"code":"15-2099.00","title":"Mathematical Science Occupations, All Other","slug":"mathematical-science-occupations-all-other"}],"industries":[{"code":"541990","title":"All Other Professional, Scientific, and Technical Services","slug":"all-other-professional-scientific-and-technical-services"},{"code":"541690","title":"Other Scientific and Technical Consulting Services","slug":"other-scientific-and-technical-consulting-services"},{"code":"541715","title":"Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)","slug":"research-and-development-in-the-physical-engineering-and-life-sciences-except-nanotechnology-and-biotechnology"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Quantitative Meteorologist","description":"About Rainmaker\nRainmaker is pioneering a modern cloud-seeding system to increase precipitation, improve water availability, and address severe-weather challenges. We combine atmospheric science, weather-resistant UAS, radar and satellite observations, numerical weather prediction, novel sensing systems, and sustainable seeding technologies to design, operate, and evaluate precipitation-enhancement programs.\n\nResearch at Rainmaker is attached directly to operations. Our scientists and engineers collect proprietary observations, deliberately intervene in atmospheric systems, evaluate the results, and use what they learn to improve the next operation.\n\nAbout the Role\nAs a Quantitative Meteorologist, you will bridge atmospheric science, statistical analysis, operational decision-making, and commercial program design.\n\nYou will develop rigorous methods for identifying when and where cloud-seeding operations are most likely to be effective, evaluating completed operations, improving real-time forecast and nowcast workflows, and assessing potential new programs. You will turn meteorological expertise that currently lives in individual judgment into repeatable analyses, decision systems, and defensible measures of performance.\n\nThis is not primarily a shift-forecasting role or a pure academic-research position. You will own ambiguous quantitative questions that span science, operations, product, and business development, and you will personally build the analyses and tools needed to answer them.\n\nWhat You'll Do\n\nDevelop quantitative methods for identifying, scoring, and ranking cloud-seeding opportunities.\n\nAnalyze historical and real-time meteorological data to understand the atmospheric and operational conditions associated with successful targeting and precipitation outcomes.\n\nDesign observational studies, experiments, and statistical analyses that distinguish intervention effects from natural weather variability as rigorously as the available data permits.\n\nEstablish honest uncertainty bounds and communicate when the evidence does not support a causal conclusion.\n\nBuild reusable tools for evaluating potential cloud-seeding programs, including climatology, seedable-hour frequency, targetability, operating constraints, expected opportunity, program design, and sensitivity analysis.\n\nWork with software engineers to automate meteorological forecasting and nowcasting workflows used by flight and field operations.\n\nDevelop decision-support methods that combine NWP, ensembles, radar, satellite, sounding, aircraft, UAS, surface, and in-situ observations.\n\nDefine ground truth, baselines, validation methods, and performance metrics for forecasting, retrieval, precipitation-estimation, and intervention-analysis systems.\n\nTranslate meteorological concepts into features, labels, physical constraints, evaluation frameworks, and failure cases for machine-learning work.\n\nWork with ML and software engineers on hybrid physical, statistical, and learning-based approaches while retaining responsibility for meteorological validity.\n\nProduce technical analyses that support customer proposals, program design, business development, scientific validation, and operational reviews.\n\nCreate stronger feedback loops between forecasting, field operations, sensor development, research, and model development.\n\nCommunicate results clearly to scientists, operators, engineers, customers, regulators, and nontechnical stakeholders.\n\nWhat We're Looking For\n\nAn advanced degree in meteorology, atmospheric science, applied mathematics, statistics, physics, or a related quantitative field, or equivalent evidence of exceptional quantitative meteorological ability.\n\nStrong understanding of cloud and precipitation processes, mesoscale meteorology, and numerical weather prediction.\n\nExperience applying statistical methods to noisy, spatially and temporally correlated environmental data.\n\nStrong Python and scientific-computing skills, including experience with tools such as NumPy, SciPy, pandas, xarray, and geospatial libraries.\n\nExperience working with meteorological data such as GRIB, netCDF, radar, satellite, model, sounding, aircraft, or surface observations.\n\nAbility to formulate ambiguous scientific and operational questions as measurable quantitative problems.\n\nExperience building reproducible analyses, automated workflows, datasets, or decision-support tools.\n\nStrong judgment about causality, confounding, uncertainty, validation, and the limits of observational evidence.\n\nClear written and verbal communication across scientific, operational, engineering, and commercial teams.\n\nHigh agency and willingness to do the analytical and implementation work personally.\n\nWe care deeply about demonstrated technical ownership. If you have a project, system, experiment, paper, portfolio, or technical write-up that shows how you approach difficult problems, include it with your application and tell us what you personally contributed.\n\nPreferred Qualifications\n\nA PhD in meteorology, atmospheric science, or a closely related field.\n\nExperience with cloud microphysics, orographic precipitation, convective precipitation, weather modification, hail, or field campaigns.\n\nExperience with WRF, HRRR, GFS, ECMWF products, data assimilation, ensembles, operational forecast verification, or meteorological post-processing.\n\nExperience with causal inference, experimental design, Bayesian methods, spatial statistics, time-series analysis, uncertainty quantification, or decision science.\n\nExperience developing statistical or ML models for weather, remote sensing, or physical systems.\n\nFamiliarity with radar meteorology, satellite retrievals, quantitative precipitation estimation, cloud-particle measurements, or atmospheric instrumentation.\n\nExperience designing or evaluating operational meteorological programs.\n\nExperience communicating quantitative results to customers, regulators, government agencies, or business-development teams.\n\nWhat Success Looks Like\n\nWithin your first year, you will have helped Rainmaker:\n\nQuantify and rank cloud-seeding opportunities more consistently.\n\nImprove the accuracy, speed, and automation of operational forecasting and nowcasting.\n\nEstablish repeatable and scientifically defensible methods for evaluating operational outcomes.\n\nIdentify changes to targeting or program design that can increase expected precipitation yield.\n\nEvaluate new regions and customer programs using rigorous meteorological and quantitative analysis.\n\nDefine better ground truth and evaluation frameworks for Rainmaker's ML, retrieval, and forecasting systems.\n\nCreate durable feedback loops between field operations, scientific research, commercial program design, and model development.\n\nBenefits\n\nSignificant stock options with high potential upside as an early-stage company\n\n401(k) with employer matching\n\nFull health coverage (medical, dental, and vision insurance)\n\nRelocation assistance provided (if applicable)\n\nUnlimited PTO\n\nPaid parental leave for both parents\n\nLunch provided when working in-office and a fully stocked kitchenette\n\nFree EV charging at the HQ\n\n#J-18808-Ljbffr","datePosted":"2026-08-31T03:17:52.688Z","dateModified":"2026-08-31T03:17:52.688Z","hiringOrganization":{"@type":"Organization","name":"Socket","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"El Segundo","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"27e46e64f9d23c6fdf7b226b"},"url":"https://jobsearcher.com/jobs/27e46e64f9d23c6fdf7b226b"}}