Data Scientist, Preparedness

What You’ll Do

• Evaluate and improve mitigation systems, including classifiers and detection pipelines across domains (e.g., biosecurity, cybersecurity, emerging risk areas).
• Diagnose false positives and false negatives with deep error analysis, root cause investigation, and clear recommendations.
• Build monitoring and measurement frameworks to track mitigation effectiveness over time and across user segments/use cases.
• Identify trends in over-blocking vs. under-blocking, quantify customer impact, and propose prioritized interventions.
• Develop insights from customer feedback, complaints, and usage patterns to detect shifts in adversarial behavior and system failure modes.
• Expand risk monitoring into new areas, including cybersecurity threats and model loss-of-control/sabotage scenarios, partnering with domain experts.
• Communicate results to technical/executive stakeholders with crisp narratives, decision-ready metrics, and clear tradeoffs.

Qualifications

• Significant experience in data science or applied analytics in high-stakes domains (e.g., security, trust & safety, abuse prevention, fraud, platform integrity, reliability).
• Strong foundations in experimentation, causal thinking, observational inference; design robust measurement under imperfect data.
• Fluency in SQL and Python (or equivalent) for analysis, modeling, monitoring workflows.
• Experience building metrics, dashboards, operational monitoring that changes outcomes.
• Track record driving cross-functional impact with engineering, product, research partners.

Nice-to-haves:

• Cybersecurity data science experience (threat modeling, adversarial dynamics, abuse patterns, security telemetry).
• Classifier evaluation, calibration, thresholding, error analysis at scale; familiarity with detection systems in adversarial settings.
• Trust & Safety experience.
• Interest in AI safety, alignment, catastrophic risk prevention.