Quantitative Meteorologist

About Rainmaker

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. 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.

As a Quantitative Meteorologist, you will bridge atmospheric science, statistical analysis, operational decision-making, and commercial program design.

You 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.

This 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.

• 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 now casting 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.
• 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 Num Py, Sci Py, 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…