Senior Scientist, Data Assimilation – Observing Systems

• Lead work to determine what observations are needed to improve aerosol microphysical models and design decision‑relevant outdoor experiments.
• Develop an inverse modeling framework to use SABRE, ATo M, 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.

Requirements

• Ph D 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 to real‑world observing requirements.
• Excellent written and verbal communication skills, especially with mixed scientific, engineering, and non‑technical audiences.
• You’re creative and attached to outcomes, not process — you’re constantly looking for new paths to the destination and excited to switch gears if there’s a faster, better way to get something done.
• You are low ego, and have a proven track record for working well across disciplines and with external partners.
• 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, ATo M, 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.
• Core Competencies

Demonstrates expertise in atmospheric science and aerosol modeling, with strong capabilities in inverse modeling, data assimilation, and optimization techniques.

Proficient in scientific programming with Python and experienced in designing observational experiments and data processing workflows.

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