Data Scientist, Safety

OpenAI's Safety teams work to ensure our products are safe, trusted, and resilient as frontier AI systems scale globally. We tackle some of the company's most important challenges across understanding and preventing misuse and misalignment, intercepting fraud and abuse, and protecting vulnerable users.

We are hiring Data Scientists to help build the analytical foundations that allow OpenAI to deploy increasingly capable AI responsibly. We are hiring Data Scientists across several teams that contribute to safety in different ways, including:

This is a high-impact role operating at the intersection of product, safety, policy, and research.

As a Data Scientist, Safety, you will help solve complex and ambiguous problems where rigorous analysis directly informs critical decisions.

Depending on your background and team alignment, you may work on areas such as:

- Measure harmful or abusive behavior across OpenAI's products

- Evaluate and improve safety classifiers, rules systems, mitigation systems, and human review workflows

- Design experiments and causal analyses to understand product, policy, and mitigation impacts

- Build prevalence estimators, dashboards, monitoring systems, and executive decision frameworks

- Diagnose gaps in safety and integrity systems using behavioral and product data, and help quantify and navigate false positive / false negative tradeoffs

- Translate ambiguous safety risks into measurable problems and evidence-based recommendations

- Partner with Product, Engineering, Policy, Research, and Operations teams to improve safety outcomes

- Build zero-to-one analytical systems in rapidly evolving domains

We're looking for strong Data Scientists who thrive in ambiguous, high-leverage environments.

- Experience with experimentation, causal inference, or observational analysis

- Experience working with messy, incomplete, or noisy datasets

- Excellent communication with technical and non-technical stakeholders

- Biosecurity, synthetic biology, infectious diseases, or computational biology

- Solve novel problems where there are no existing playbooks

San Francisco or New York depending on team alignment and business need.