Applied Scientist, Leo Security

We are looking for an Applied Scientist to join the founding cohort of the Engineering and R&D team within Leo Infrastructure and IP Security. The team defends the manufacturing lines, launch sites, and global ground infrastructure behind the constellation from the most sophisticated threat actors on the planet. The data is unlike anything you have worked with: badge and door-access events, asset movement, network telemetry, and industrial control signals from factories, ground stations, and launch facilities, all of which must be modeled, baselined, and defended. You will build the statistical and behavioral models that separate threat actor behavior from the noise of a global operation, and the privacy-preserving data representations that let detection science scale without exposing sensitive data. This is an R&D role with a production mandate: every model you build becomes part of the system Leo's security teams use to protect the constellation.

- Build behavioral and statistical models that baseline normal activity across heterogeneous security telemetry, including specialized industrial-control and factory-floor data sources, so detections extend to new environments with low false-positive rates.

- Design privacy-preserving representations of sensitive security data, and verify that models and detections tuned against them remain accurate against the real data — turning data-protection guarantees into measurable, provable properties rather than assertions.

- Define the methodology and own the analysis for difficult, loosely defined problems: gather complex data across domains, select the right techniques from a range of data science methods, and justify your approach with evidence.

- Develop the metrics and evaluation frameworks that measure model and detection performance against threat actor behavior before a model is trusted in production.

- Contribute to the team's neurosymbolic reasoning platform, adapting state-of-the-art techniques from the literature and shipping components at production quality.

- Document your work with the rigor of a peer-reviewed publication, and communicate results clearly to both scientific and security-operations audiences.