Satellite Data Scientist—Retrievals & Operational Analytics

Blackwing; El Segundo (CA); Hybrid; USD 120,000 - 180,000; Full time

Benefits offered by this job

Significant stock options

401(k) with employer matching

Full health coverage

Relocation assistance

Unlimited PTO

Parental leave

Lunch provided in-office

EV charging at HQ

Job summary

Rainmaker seeks a Satellite Remote Sensing Researcher to expand satellite observations across research and operations. You will join experienced satellite scientists and add scientific capacity and stronger computational execution to the team.

Your work may include developing and validating retrievals, building automated data products, supporting operational cloud analysis, fusing satellite observations with radar and aircraft data, and pursuing applied research under guidance.

Qualifications

• Degree in atmospheric science, meteorology, remote sensing, physics, applied mathematics, electrical engineering, CS, or related field.
• Strong understanding of satellite observations, radiative transfer, retrievals, calibration, validation, or uncertainty.

Responsibilities

• Develop, reproduce, improve, and validate satellite retrieval algorithms for clouds, precipitation, atmospheric state, and cloud-seeding opportunities.
• Work with microwave sounders and polar-orbiting observations, incorporating geostationary imagery for coverage and context.
• Build automated, documented workflows that transform raw observations into scientific and operational products.
• Evaluate SLW, cloud-phase, cloud-top, moisture, temperature, precipitation, and related products against independent observations.
• Collocate satellite data with radar, NWP, soundings, surface observations, and Rainmaker platforms.
• Quantify retrieval bias, uncertainty, representativeness, latency, coverage, and failure modes by meteorological regime.
• Support real-time and retrospective cloud analysis for cloud-seeding operations.
• Improve reliability, speed, and usability of satellite-data processing across the team.
• Contribute to research directions with Rainmaker's satellite scientists.
• Collaborate with ML engineers when data-driven retrievals or multimodal models are justified.
• Work with software engineers to transition research workflows into reliable operational systems.
• Communicate results, limitations, and uncertainty clearly to researchers, operators, and engineers.

Skills

Python

Quantitative analysis

Satellite data processing

Scientific methods

Reproducible workflows

ML data integration

Strong agency/learning velocity

Education

Advanced degree in atmospheric science, remote sensing, physics, or related field

Tools

NumPy

SciPy

pandas

xarray

Dask

Job description

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.

About the Role

Rainmaker is hiring a Satellite Remote Sensing Researcher to expand how we use satellite observations across research and operations. You will join experienced satellite scientists and add both scientific capacity and stronger computational execution to the team.

Your work may include developing and validating retrievals, building automated data products, supporting operational cloud analysis, fusing satellite observations with radar and aircraft data, and pursuing applied research under the guidance of Rainmaker's existing researchers. Our highest-priority observations include microwave sounders and other polar-orbiting instruments, while geostationary imagery provides the coverage and temporal continuity needed for operations.

We are open to an earlier-career scientist with excellent fundamentals, high agency, and evidence of strong computational work. You should be excited to learn from experienced researchers while independently owning bounded projects and turning scientific methods into durable workflows.

What You'll Do

• Develop, reproduce, improve, and validate satellite retrieval algorithms relevant to clouds, precipitation, atmospheric state, and cloud-seeding opportunities.

• Work extensively with microwave sounders and other polar-orbiting observations while incorporating geostationary imagery for coverage, temporal context, and operational monitoring.

• Build automated, documented workflows that transform raw or low-level observations into useful scientific and operational products.

• Evaluate existing SLW, cloud-phase, cloud-top, moisture, temperature, precipitation, and related products against independent observations.

• Collocate satellite data with radar, NWP, soundings, surface observations, and Rainmaker aircraft, UAS, or in-situ measurements.

• Quantify retrieval bias, uncertainty, spatial representativeness, latency, coverage, and failure modes by meteorological regime.

• Support real-time and retrospective cloud analysis for cloud-seeding operations.

• Improve the reliability, speed, scalability, and usability of satellite-data processing across the science team.

• Contribute to research directions selected with Rainmaker's existing satellite scientists.

• Work with ML engineers when data-driven retrievals or multimodal models are justified by the data.

• Work with software engineers to transition successful research workflows into reliable operational systems.

• Communicate scientific results, limitations, and uncertainty clearly to researchers, operators, and engineers.

What We're Looking For

• A degree in atmospheric science, meteorology, remote sensing, physics, applied mathematics, electrical engineering, computer science, or a related field, or equivalent evidence of exceptional remote-sensing ability.

• Strong scientific understanding of satellite observations, radiative transfer, retrievals, calibration, validation, or measurement uncertainty.

• Strong Python and quantitative-analysis skills.

• Experience working computationally with satellite data, preferably including microwave or polar-orbiting observations.

• Ability to implement, evaluate, and document scientific methods personally rather than immediately handing them off to a software team.

• Experience with multidimensional scientific data and tools such as NumPy, SciPy, pandas, xarray, Dask, or equivalent systems.

• Ability to build reproducible data-processing and validation workflows.

• Comfort receiving research guidance while independently owning a clearly defined project.

• High agency, learning velocity, and willingness to engage with operational users of the resulting products.

Preferred Qualifications

• Graduate research or industry experience in satellite meteorology, microwave remote sensing, cloud or precipitation retrievals, atmospheric sounding, or Earth observation.

• Experience working with both polar-orbiting and geostationary observations.

• Familiarity with radiative-transfer models, retrieval inversion, Bayesian estimation, uncertainty quantification, or statistical and ML retrieval methods.

• Experience collocating satellite data with radar, aircraft, in-situ sensors, soundings, or NWP.

• Familiarity with cloud microphysics, mixed-phase clouds, supercooled liquid water, precipitation processes, or weather modification.

• Experience with cloud-scale or large-volume geospatial processing in local, HPC, or cloud environments.

• Experience creating data products used in operational forecasting or decision-making.

What Success Looks Like

• Within your first three months, you will have taken ownership of one bounded retrieval or satellite-data workflow selected with the existing team. You will have reproduced and validated the current baseline, then delivered a meaningful improvement in retrieval quality, automation, coverage, latency, scalability, or operational usability.

  The result will be a documented and repeatable workflow that Rainmaker's broader science or operations team can use. Success does not require inventing a novel retrieval in one quarter; it requires producing trustworthy computational leverage and demonstrating clear scientific judgment.

  Within your first year, you will have expanded the team's capacity across microwave, polar-orbiting, and geostationary observations while making multiple satellite workflows more validated, automated, and operationally useful.

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