Data Scientist, Preparedness
About the Team The Preparedness team is an important part of the Safety Systems org at OpenAI, and is guided by OpenAI’s Preparedness Framework . Frontier AI models have the potential to benefit all of humanity, but also pose increasingly severe risks.
What this role actually needs.
Data Scientist, Preparedness at OpenAI in San Francisco. UpJobz keeps this listing high-signal for applicants targeting serious high-tech roles across the United States, Canada, and Mexico. About the Team The Preparedness team is an important part of the Safety Systems org at OpenAI, and is guided by OpenAI’s Preparedness Framework . Frontier AI models have the potential to benefit all of humanity, but also pose increasingly severe risks.
Day-to-day expectations
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- Evaluate and improve mitigation systems, including classifiers and detection pipelines across domains (e.g., biosecurity, cybersecurity, and emerging risk areas).
- Diagnose false positives and false negatives with deep error analysis, root cause investigation, and clear recommendations for mitigation adjustments.
- Build monitoring and measurement frameworks to track mitigation effectiveness over time and across user segments and 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 or sabotage scenarios, in partnership with domain experts.
What a strong candidate brings
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- 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.
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- Tailor your resume around ai and research instead of sending a generic application.
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