Fire Science Intern

🇺🇸 San Francisco, California
Posted 2 months ago
Expires May 29, 2026

Why Join Stand: At Stand, you’ll help build a new class of global property protection. We use advanced physics and AI to model catastrophic risk at the asset level, then automate underwriting and mitigation before loss occurs. Insurance is simply the current delivery mechanism. The real product is a scalable risk engine.

We stay when traditional insurers exit. We model what others approximate. And we build systems that change outcomes, not just prices.

Background: The property insurance industry is built to price loss after it happens. It relies on coarse proxies, backward-looking data, and manual processes, then accepts damage as unavoidable.

Stand takes a different approach. We simulate how real-world catastrophes affect individual properties, translate that into actionable decisions, and automate the business around it. The result is a platform that can underwrite what others can’t and operate with far less friction.

About Stand Insurance: Stand is a technology and insurance company reimagining how society assesses, mitigates, and adapts to climate risk for homeowners. Our leadership team brings deep experience across insurance, technology, and climate science, having built significant market value at prior ventures.

Traditional insurance models often rely on broad exclusions, leaving many homeowners with limited or no viable coverage options. At Stand, we use deterministic, physics-based models and advanced analytics to deliver personalized risk assessments - empowering homeowners to secure coverage and take proactive, science-driven steps toward resilience.

Why Join Stand: At Stand, you’ll join a mission-driven team redefining insurance through the lens of climate resilience, building a transformative, data-driven insurance model with real-world impact for homeowners and communities on the front lines of climate change.

Background: Most property insurers assess wildfire risk using broad proxies, backward-looking loss da...

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