Application Guide

How to Apply for Senior Scientist, Data Assimilation for Observing Systems

at Reflective

🏢 About Reflective

Reflective is a philanthropically-funded initiative dedicated to advancing sunlight reflection research and technology development. Unlike commercial entities, its mission-driven focus on climate solutions offers scientists a unique opportunity to work on high-impact, open-science projects without profit constraints. The remote-first culture emphasizes collaboration across global teams.

About This Role

This role involves leading the development of inverse modeling frameworks to improve aerosol microphysical models using real-world data from field campaigns like SABRE and AToM. You'll design experiments (data denial, OSSEs) to optimize instrument suites for outdoor tests, directly influencing the measurement strategies for solar radiation management research. The work bridges modeling and observational data, providing critical insights for climate intervention assessments.

💡 A Day in the Life

A typical day might start with a stand-up meeting with the distributed team to discuss progress on inverse modeling frameworks. You'll spend the morning coding in Python, running data denial experiments on field campaign data, and analyzing results. Afternoons could involve writing a scientific paper, reviewing model outputs, or coordinating with collaborators on observational requirements for upcoming experiments.

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🎯 Who Reflective Is Looking For

  • PhD in atmospheric science, aerosol science, applied math, or related field with a strong publication record in data assimilation or inverse modeling.
  • Proven experience handling large atmospheric observational datasets, especially in situ aircraft data from field campaigns (e.g., SABRE, AToM, or similar).
  • Expertise in Python for scientific computing, including libraries like NumPy, SciPy, xarray, and Dask, and familiarity with data assimilation frameworks (e.g., DART, PyMC).
  • Ability to translate model uncertainty into actionable observational requirements and communicate findings to interdisciplinary teams.

📝 Tips for Applying to Reflective

1

Highlight specific experience with SABRE, AToM, or similar field campaign data in your resume and cover letter.

2

Demonstrate your proficiency with Python by linking to relevant GitHub repositories or projects involving large environmental datasets.

3

In your cover letter, explicitly connect your inverse modeling skills to improving aerosol microphysical models, using concrete examples.

4

Mention any experience with Observing System Simulation Experiments (OSSEs) or data denial experiments, as these are central to the role.

5

Tailor your application to emphasize your ability to work remotely and collaborate across time zones, as the team is distributed.

✉️ What to Emphasize in Your Cover Letter

['Your experience with data assimilation or inverse modeling applied to atmospheric aerosols.', 'Your familiarity with observational datasets from aircraft or field campaigns, especially SABRE/AToM.', 'Your ability to design experiments (e.g., OSSEs) that inform instrument requirements.', "Your passion for climate intervention research and alignment with Reflective's mission."]

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Read Reflective's published research papers or blog posts on sunlight reflection and aerosol microphysics.
  • → Understand the SABRE and AToM field campaigns: their objectives, instruments, and data products.
  • → Review the company's organizational structure and funding model to understand its unique position in the climate field.
  • → Familiarize yourself with current debates in solar radiation management to show informed interest.

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Describe your experience with inverse modeling or data assimilation in aerosol science. What challenges did you face?
2 How would you design an OSSE to determine the minimum viable instrument suite for a field experiment?
3 Explain how you would handle quality control and rapid model updates for large field campaign datasets.
4 How do you translate model uncertainty into observational requirements? Provide an example.
5 What is your experience with Python for processing large environmental datasets? Discuss specific libraries or workflows.
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⚠️ Common Mistakes to Avoid

  • Submitting a generic cover letter that doesn't mention specific field campaigns (SABRE, AToM) or data assimilation.
  • Overlooking the remote aspect: failing to demonstrate self-motivation and communication skills for distributed work.
  • Ignoring the philanthropic, mission-driven nature: avoid focusing solely on personal career advancement without expressing commitment to climate solutions.

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

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Good luck with your application to Reflective!

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