About the role:

Work closely with founders to quickly build an understanding of the product + take increasing ownership over key modeling decisions as the platform matures. Our company's success depends on accurately predicting losses in the hardware we insure. This engineer will play a foundational role in building and evolving these systems:

- Build and improve portfolio-level risk models (frequency, severity, expected loss, tail risk) using a mix of technical and operational signals

- Run scenario analysis and catastrophe/stress testing to ensure healthy risk dispersion.

- Continuously calibrate and improve models using new underwriting data, post-bind performance, and claims signals.

- Own, evolve, and refine the core risk engine that sits behind policy issuance and pricing.

Qualifications:

Experience: 3–5+ years (industry or grad school) applying data science, risk modeling, reliability analysis, or predictive modeling to physical systems, including but not limited to robotics/autonomy and energy systems.

Background: Data science, reliability/systems engineering, applied statistics, or modeling roles; demonstrated experience analyzing and quantifying physical system behavior, performance, and faults from real-world data.

Interview Process

The interview process will mimic what it’s like to work together. We will talk through and work on real problems that the company is facing to understand your capabilities.