Quantitative Meteorologist

About the role

Rainmaker 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. Research at Rainmaker is attached directly to operations.

What You'll Do

• Develop quantitative methods for identifying, scoring, and ranking cloud-seeding opportunities.
• Analyze historical and real-time meteorological data to understand the atmospheric and operational conditions associated with successful targeting and precipitation outcomes.
• 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.
• Work with software engineers to automate meteorological forecasting and nowcasting workflows used by flight and field operations.
• Develop decision-support methods that combine NWP, ensembles, radar, satellite, sounding, aircraft, UAS, surface, and in-situ observations.
• Define ground truth, baselines, validation methods, and performance metrics for forecasting, retrieval, precipitation-estimation, and intervention-analysis systems.
• Translate meteorological concepts into features, labels, physical constraints, evaluation frameworks, and failure cases for machine-learning work.
• Work with ML and software engineers on hybrid physical, statistical, and learning-based approaches while retaining responsibility for meteorological validity.
• 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.
• Communicate results clearly to scientists, operators, engineers, customers, regulators, and nontechnical stakeholders.

What We're Looking For

• An advanced degree in meteorology, atmospheric science, applied mathematics, statistics, physics, or a related quantitative field, or equivalent evidence of exceptional quantitative meteorological ability.
• A 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.
• The ability to formulate ambiguous scientific and operational questions as measurable quantitative problems.
• Experience building reproducible analyses, automated workflows, datasets, or decision-support tools.
• Strong judgment about causality, confounding, uncertainty, validation, and the limits of observational evidence.
• Clear written and verbal communication across scientific, operational, engineering, and commercial teams.
• A high agency and willingness to do the analytical and implementation work personally.

Preferred Qualifications

• 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.
• Experience developing statistical or ML models for weather, remote sensing, or physical systems.
• Familiarity with radar meteorology, satellite retrievals, quantitative precipitation estimation, cloud-particle measurements, or atmospheric instrumentation.
• Experience designing or evaluating operational meteorological programs.
• Experience communicating quantitative results to customers, regulators, government agencies, or business-development teams.

Benefits

• Significant stock options with high potential upside as an early-stage company
• 401(k) with employer matching
• Full health coverage (medical, dental, and vision insurance)
• Relocation assistance provided (if applicable)
• Unlimited PTO
• Paid parental leave for both parents
• Lunch provided when working in-office and a fully stocked kitchenette
• Free EV charging at the HQ