Senior Scientist, Data Assimilation for Observing Systems

• Lead the development of an inverse modeling framework to improve aerosol microphysical models using observational datasets like SABRE and AToM.
• Design and run data denial experiments and Observing System Simulation Experiments (OSSEs) to determine minimum viable instrument suites for outdoor field experiments.
• Build data pipelines and processing workflows for field experiment data, ensuring rapid quality control and model updates.
• Translate model uncertainty into concrete observational requirements and communicate findings through scientific papers and summaries.

This role directly advances sunlight reflection research, which may be critical for rapidly limiting climate impacts. By improving aerosol microphysical models and designing decision-relevant experiments, you will provide the data needed for informed decisions about potential deployment of solar radiation management.

• PhD in atmospheric science, aerosol science, applied math, engineering, Earth system science, or related field.
• Significant experience with atmospheric observational datasets, especially in situ data from aircraft or field campaigns.
• Experience with inverse modeling, data assimilation, optimization, or uncertainty quantification.
• Strong scientific programming skills in Python and experience with large environmental datasets.

Annual salary between $130,000 and $180,000 USD, depending on experience. Benefits include medical, dental, vision insurance, 401(k), professional development, generous PTO and sick leave (12 weeks paid parental leave), and flexible working hours.

Reflective is a global, non-profit research organization aiming to radically accelerate the pace of sunlight reflection research, equipping the world with data and tools to make informed decisions about sunlight reflection fast enough to matter.