Reflective
Senior Scientist, Observing Systems & Inverse Modeling
Remote or San Francisco, California, United States · Senior · Full-time
Sponsorship not specifiedDetected 15 days ago
PythonData AnalysisData ScienceResearchCommunicationCollaborationMentoring
About the role
- 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.
Responsibilities
- As Reflective's Senior Scientist, Observing Systems & Inverse Modeling, you will lead the design of observation strategies and inverse modeling workflows to determine what measurements are needed for SAI field experiments.
- 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.
- Build data-processing workflows for future scientific field-experiment data, ensuring that data can be rapidly quality-controlled, analyzed, and used to update model parameterizations.
- 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.
- 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.
- 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.
Requirements
- 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.
- You are passionate about Reflective's mission.
- Experience with adjoint methods, variational data assimilation, gradient-based optimization, or other approaches for high-dimensional parameter estimation.
- Familiarity with datasets from SABRE, AToM, or similar atmospheric chemistry / aerosol missions.
- Experience designing or running OSSEs, OSEs, data denial experiments, or observing network optimization studies.
- Experience with JAX or with building adjoints with automatic differentiation
- We care more about the underlying technical skillset, scientific judgment, and ability to learn quickly.
Nice to have
- Strongly preferred
Skills
- Reflective is a global, non-profit research organization aiming to radically accelerate the pace of sunlight reflection research.
Compensation
- Compensation and Benefits
Benefits
- This is a specialized scientific role, not a general data science, analytics, or machine-learning role.
- Medical, dental, vision insurance
- Generous paid time off and sick leave, including 12 weeks paid parental leave
- Flexible working hours
Visa & Work Authorization
- We may be able to sponsor visas for US-based foreign nationals and have a moving stipend to support candidates who would like to relocate to the Bay Area
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This listing is sourced directly from Reflective's careers page and normalized into a canonical job model.