Rainmaker Technology Corporation
Quantitative Meteorologist
El Segundo, CA
Sponsorship not specifiedDetected 23 hours ago
PythonMachine LearningPandasNumPyStatisticsBusiness DevelopmentForecastingSensorsResearchExperimental DesignCommunicationRemote Sensing
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Cap-exempt (no lottery)0
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Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
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About the role
- You will turn meteorological expertise that currently lives in individual judgment into repeatable analyses, decision systems, and defensible measures of performance.
- This is not primarily a shift-forecasting role or a pure academic-research position.
Responsibilities
- Develop quantitative methods for identifying, scoring, and ranking cloud-seeding opportunities.
- Design observational studies, experiments, and statistical analyses that distinguish intervention effects from natural weather variability as rigorously as the available data permits.
- Establish honest uncertainty bounds and communicate when the evidence does not support a causal conclusion.
- Build reusable tools for evaluating potential cloud-seeding programs, including climatology, seedable-hour frequency, targetability, operating constraints, expected opportunity, program design, and sensitivity analysis.
- Develop decision-support methods that combine NWP, ensembles, radar, satellite, sounding, aircraft, UAS, surface, and in-situ observations.
- Produce technical analyses that support customer proposals, program design, business development, scientific validation, and operational reviews.
- Create stronger feedback loops between forecasting, field operations, sensor development, research, and model development.
Requirements
- An advanced degree in meteorology, atmospheric science, applied mathematics, statistics, physics, or a related quantitative field, or equivalent evidence of exceptional quantitative meteorological ability.
- Strong understanding of cloud and precipitation processes, mesoscale meteorology, and numerical weather prediction.
- Experience applying statistical methods to noisy, spatially and temporally correlated environmental data.
- Strong Python and scientific-computing skills, including experience with tools such as NumPy, SciPy, pandas, xarray, and geospatial libraries.
- Experience working with meteorological data such as GRIB, netCDF, radar, satellite, model, sounding, aircraft, or surface observations.
- Ability to formulate ambiguous scientific and operational questions as measurable quantitative problems.
- 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.
Nice to have
- A PhD in meteorology, atmospheric science, or a closely related field.
- Experience with cloud microphysics, orographic precipitation, convective precipitation, weather modification, hail, or field campaigns.
- Experience with WRF, HRRR, GFS, ECMWF products, data assimilation, ensembles, operational forecast verification, or meteorological post-processing.
- Experience with causal inference, experimental design, Bayesian methods, spatial statistics, time-series analysis, uncertainty quantification, or decision science.
- Familiarity with radar meteorology, satellite retrievals, quantitative precipitation estimation, cloud-particle measurements, or atmospheric instrumentation.
- Experience designing or evaluating operational meteorological programs.
- What Success Looks Like
- Within your first year, you will have helped Rainmaker:
Skills
- Research at Rainmaker is attached directly to operations.
Benefits
- Significant stock options with high potential upside as an early-stage company
- Full health coverage (medical, dental, and vision insurance)
- Paid parental leave for both parents
Company info
- Communicate results clearly to scientists, operators, engineers, customers, regulators, and nontechnical stakeholders.
- What We're Looking For
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This listing is sourced directly from Rainmaker Technology Corporation's careers page and normalized into a canonical job model.