Senior Scientist, Data Assimilation – Observing Systems

This is a fully remote position, open to applicants in California.

📋 Description

• Lead efforts to identify the observations necessary for enhancing aerosol microphysical models and create decision-relevant outdoor experiments.

• Establish an inverse modeling framework utilizing SABRE, AToM, and other pertinent in situ observational datasets to refine existing aerosol microphysical models, potentially incorporating adjoint-based techniques.

• Design and execute data denial experiments to ascertain which observations are crucial for constraining microphysical parameters, aiding in the definition of a minimum viable instrument suite for future outdoor field experiments.

• Create formal Observing System Simulation Experiments (OSSEs) that replicate observations of an aerosol plume under various potential instrument suites, sampling strategies, and cadences to evaluate the marginal value of different measurement techniques.

• Conduct OSSE analyses across diverse plume conditions, atmospheric states, and experimental configurations, and translate OSSE findings into actionable field campaign recommendations: where to sample, how frequently, at what altitude, with which instruments, and with acceptable error bounds.

• Develop data pipelines and processing workflows for upcoming field experiment data, ensuring rapid quality control, analysis, and updates to model parameterizations.

• Collaborate closely with Reflective’s science, engineering, and data teams to convert model uncertainty into specific observational requirements.

• Author scientific papers, concise memos, technical documentation, and public-facing summaries that clarify what has been learned, what uncertainties remain, and how the results should guide experimental design to funders, policymakers, researchers, and the broader field.

⛳️ Requirements

• PhD in atmospheric science, aerosol science, applied mathematics, engineering, Earth system science, or a related discipline.

• Extensive experience with atmospheric observational datasets, particularly in situ data from aircraft, field campaigns, or similar observing systems.

• Proficiency in inverse modeling, data assimilation, optimization, uncertainty quantification, or a related quantitative methodology.

• Strong programming skills, particularly in Python, along with experience handling large and complex environmental datasets.

• Excellent quantitative reasoning skills, including the ability to analyze nonlinear systems, over-constrained inference problems, parameter identifiability, and model structural uncertainty.

• Capability to design rigorous numerical experiments that link technical modeling decisions to real-world observational needs.

• Outstanding written and verbal communication abilities, especially among mixed scientific, engineering, and non-technical audiences.

• A creative mindset focused on outcomes rather than processes, always seeking innovative pathways to achieve goals and eager to pivot if a quicker or more effective solution arises.

• A low-ego collaborative spirit, with a proven ability to work effectively across disciplines and with external partners.

• A passion for Reflective’s mission.

• Familiarity with adjoint methods, variational data assimilation, gradient-based optimization, or other high-dimensional parameter estimation techniques.

• Knowledge of datasets from SABRE, AToM, or similar atmospheric chemistry/aerosol missions.

• Experience in designing or conducting OSSEs, OSEs, data denial experiments, or optimizing observing networks.

• Proficiency with JAX or in constructing adjoints using automatic differentiation.

🏝️ Benefits

• Medical, dental, and vision insurance

• 401(k)

• Opportunities for professional and personal development

• Generous paid time off and sick leave, including 12 weeks of paid parental leave

• Flexible working hours