Data Scientist, Preparedness

Highlights

• Build monitoring systems to detect extreme AI harms early and measure mitigations.
• Improve classifiers and detection pipelines for biosecurity and cybersecurity risks.
• Expand risk monitoring into loss-of-control and sabotage scenarios with experts.

What they offer

• equity

Ideal candidate

Autonomous, impact-driven data scientist with high-stakes trust and safety experience who cares about preventing catastrophic AI misuse and turning ambiguous analysis into safer products.

Requirements

• Significant experience in data science or applied analytics in high-stakes domains (e.g., security, trust & safety, abuse prevention, fraud, platform integrity, or reliability).
• Strong foundations in experimentation, causal thinking, and/or observational inference; ability to design robust measurement under imperfect data.
• Fluency in SQL and Python (or equivalent) for analysis, modeling, and building monitoring workflows.
• Experience building metrics, dashboards, and operational monitoring that meaningfully changes outcomes (not just reporting).
• Track record of driving cross-functional impact with engineering, product, and research partners.
• Cybersecurity data science experience (strong preference), including exposure to threat modeling, adversarial dynamics, abuse patterns, or security telemetry.