Senior Scientist, Data Assimilation Observing Systems

Senior Scientist, Data Assimilation for Observing Systems

Remote or San Francisco, California, United States

Sunlight reflection may be the only available option, alongside dramatic emissions reductions, adaptation, and rapid scaling of carbon removal, to rapidly limit many climate impacts over the coming decades. But we don't know nearly enough about it to make a scientifically-informed decision about potential deployment – and we're not on a trajectory for rapid, legitimate decision making.

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

What you'll do

As Reflective's Senior Scientist, Data Assimilation for Observing Systems, you will lead our work to determine what observations are needed to improve aerosol microphysical models and design decision-relevant outdoor experiments.

This role sits at the intersection of atmospheric observations, aerosol microphysics, inverse modeling, data assimilation, and field campaign design. You'll develop methods to use existing high-quality observational datasets — including SABRE, AToM, and other relevant missions — to improve microphysical model parameterizations. You'll then use those methods to determine which observations matter most, what minimum instrument suite is needed for an outdoor experiment, and how many experimental iterations may be required to meaningfully constrain model uncertainty.

Your work will be fundamental to field experiment design, and you will have primary responsibility for data analysis and model optimization after an experiment has been conducted.

Responsibilities

• Develop an inverse modeling framework to use SABRE, AToM, and other relevant in situ observational datasets to improve existing aerosol microphysical models, potentially including adjoint-based approaches.
• Design and run data denial experiments to determine which observations are most important for constraining microphysical parameters to help define a minimum viable instrument suite for a future outdoor field experiment.
• Develop formal Observing System Simulation Experiments (OSSEs) that simulate observations of an aerosol plume under different potential instrument suites, sampling strategies, and cadences to quantify the marginal value of different measurement strategies.
• Repeat OSSE analyses across a range of plume conditions, atmospheric states, and experimental configurations, and translate OSSE results into practical field campaign recommendations: where to sample, how often, at what altitude, with which instruments, and with what acceptable error bounds.
• Build data pipelines and processing workflows for future field experiment data, ensuring that data can be rapidly quality-controlled, analyzed, and used to update model parameterizations.
• Work closely with Reflective's science, engineering, and data teams to translate model uncertainty into concrete observational requirements.
• Write scientific papers, concise memos, technical documentation, and public-facing summaries that make what has been learned, what remains uncertain, and how the results should inform experiment design clear to funders, policymakers, researchers, and the wider field.

Who you are

Minimum qualifications

• PhD in atmospheric science, aerosol science, applied math, engineering, Earth system science, or a related field.
• Significant experience working with atmospheric observational datasets, especially in situ data from aircraft, field campaigns, or comparable observing systems.
• Experience with inverse modeling, data assimilation, optimization, uncertainty quantification, or a closely related quantitative method.
• Strong scientific programming skills, especially in Python, and experience working with large, complex environmental datasets.
• Strong quantitative judgment, including the ability to reason about nonlinear systems, over-constrained inference problems, parameter identifiability, and model structural uncertainty.
• Ability to design rigorous numerical experiments that connect technical modeling choices…