Data Scientist, Preparedness

About the Team The Preparedness team is an important part of the Safety Systems https://openai.com/safety/safety-systems org at OpenAI, and is guided by OpenAI’s Preparedness Framework https://openai.com/index/updating-our-preparedness-framework/. Frontier AI models have the potential to benefit all of humanity, but also pose increasingly severe risks. To ensure that AI promotes positive change, the Preparedness team helps us prepare for the development of increasingly capable frontier AI models. This team is tasked with identifying, tracking, and preparing for catastrophic risks related to frontier AI models. The mission of the Preparedness team is to: 1. Closely monitor and predict the evolving capabilities of frontier AI systems, with an eye towards misuse risks whose impact could be catastrophic to our society 2. Ensure we have concrete procedures, infrastructure and partnerships to mitigate these risks and to safely handle the development of powerful AI systems Preparedness tightly connects capability assessment, evaluations, and internal red teaming, and mitigations for frontier models, as well as overall coordination on AGI preparedness. This is fast paced, exciting work that has far reaching importance for the company and for society. About the Role We’re hiring a Data Scientist to help build, evaluate, and continuously improve mitigations that prevent extreme harms from AI systems. This role is for an experienced, highly autonomous individual contributor who can take ambiguous problem statements, structure rigorous analyses, and translate findings into actionable product and policy changes. This position goes beyond “running evals.” You’ll help create mitigation intelligence and monitoring systems that enable OpenAI to detect issues early, measure effectiveness over time, and reduce both over-blocking (unnecessary friction) and under-blocking (missed harm). WHAT YOU’LL DO - 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. - Communicate results to technical and executive stakeholders with crisp narratives, decision-ready metrics, and clear tradeoffs. You might thrive in this role if you are: - An autonomous operator: you can take a problem statement and independently structure the analysis end-to-end. - Strong at executive-ready communication: concise, clear, and outcome-oriented. - Skilled in turning analysis into productable changes: you’re comfortable influencing across functions to drive mitigation improvements. QUALIFICATIONS - 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. - Experience with classifier evaluation, calibration, thresholding, and error analysis at scale. Familiarity with detection systems in adversarial settings (e.g., evasion, distribution shift, feedback loops). - Trust & Safety experience is helpful, but not required. - Genuine interest in AI safety, alignment, and catastrophic risk prevention. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement https://cdn.openai.com/policies/eeo-policy-statement.pdf. Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties,