Quantitative Meteorologist

About the role

The owns the end-to-end analytical backbone of cloud-seeding program design, execution, and evaluation. You will translate ambiguous atmospheric opportunities into measurable intervention strategies and defensible performance evidence. This role requires you to frame scientific uncertainty in operational terms while maintaining rigorous meteorological integrity across all decisions. You will convert expert judgment into repeatable systems that can be audited, challenged, and improved over time. Ownership extends from hypothesis and data selection through to final recommendation and stakeholder communication. You are accountable for both the technical soundness and the commercial relevance of your analyses. Success is measured by the clarity of your methods, the robustness of your evidence, and the real-world value delivered to water-security programs. This position sits at the intersection of atmospheric science, statistical innovation, and operational execution.

Key facts

What you'll do

• Build quantitative frameworks that identify, score, and rank cloud-seeding opportunities across space and time.
• Analyze historical and real-time meteorological data to map atmospheric conditions and operational outcomes linked to successful targeting.
• Design observational studies and statistical evaluations that reduce confounding and separate intervention effects from natural weather variability.
• Define defensible uncertainty ranges and clearly communicate when evidence is insufficient to support causal claims.
• Construct reusable evaluation tools for prospective cloud-seeding programs, including climatology, seedable-hour frequency, targetability, operational constraints, expected opportunity, program design, and sensitivity testing.
• Partner with software engineers to automate meteorological forecasting and nowcasting workflows used by flight and field operations teams.
• Develop decision-support approaches that integrate numerical weather prediction, ensembles, radar, satellite, soundings, aircraft, UAS, surface, and in-situ observations.
• Establish 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 pipelines.
• Collaborate closely with machine learning and software engineers on hybrid physical, statistical, and learning-based methods while retaining responsibility for meteorological validity.
• Produce technical analyses that inform customer proposals, program design, business development, scientific validation, and operational reviews.
• Create tighter feedback loops among forecasting, field operations, sensor development, research, and model development teams.
• Present results clearly and persuasively to scientists, operators, engineers, customers, regulators, and nontechnical stakeholders.
• Continuously iterate on methods and workflows based on new evidence, operational feedback, and emerging constraints.

Requirements

• Hold an advanced degree in a quantitative field such as meteorology, atmospheric science, applied mathematics, statistics, or physics, or provide equivalent proof of outstanding quantitative meteorological skill.
• Demonstrate deep understanding of cloud and precipitation processes, mesoscale meteorology, and numerical weather prediction.
• Show experience applying statistical methods to noisy, spatially and temporally correlated environmental data.
• Exhibit strong Python and scientific-computing skills, with hands-on experience using NumPy, SciPy, pandas, xarray, and geospatial libraries.
• Have familiarity with meteorological data formats such as GRIB and netCDF, and with radar, satellite, soundings, aircraft, and surface observations.
• Prove ability to frame ambiguous scientific and operational questions as measurable quantitative problems.
• Maintain a track record of building reproducible analyses, automated workflows, datasets, or decision-support tools.
• Exercise sound judgment regarding causality, confounding, uncertainty, validation, and the limits of observational evidence.
• Communicate clearly in writing and verbally across scientific, operational, engineering, and commercial teams.
• Show high agency and willingness to perform analytical and implementation work personally when needed.

Nice to have

Only items explicitly indicated as preferred in the source are included, and the source does not specify additional nice-to-have qualifications beyond the stated requirements and responsibilities.

Practical notes

Location is El Segundo, California. The engagement is full-time. Compensation is specified as $130,000 to $170,000 USD per year. No other hours, travel, visa, or deadline details are provided in the source.