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OpenAI

Data Scientist, Safety

OpenAI is seeking a passionate Data Scientist to join the safety team, ensuring our advanced models are responsible and reliable. This role is critical for mitigating risks associated with large language models (LLMs), focusing heavily on identifying bias, improving model robustness, and developing evaluation metrics. Key responsibilities include analyzing vast datasets of model outputs to pinpoint failure modes, designing rigorous red-teaming exercises, and implementing fairness toolkits. The ideal candidate has a strong background in ML/AI ethics and the ability to translate complex technical findings into actionable product safety guidelines. You will collaborate with policy experts and research scientists to build state-of-the-art guardrails, making you integral to shaping the future of responsible AI deployment.

OpenAI San Francisco, CA (Hybrid), United States

(تازہ ترین نوکریاں، سلیبس اور ٹیسٹ کی تیاری کی مکمل معلومات)

Preparing

1200

Question set

150

Duration

Permanent

Focus areas

Machine LearningNLPSafety EngineeringData AnalysisBias Detection

People Also Ask (FAQs)

What is the last date to apply for Data Scientist, Safety?

The closing date is listed in the official advertisement — apply as early as possible.

How do I apply for Data Scientist, Safety?

Use the official apply link on this page to submit your application. A step-by-step how-to-apply guide for this job is also linked below.

Is there negative marking in the OpenAI test?

The marking rule is announced per test in the advertisement and roll-number-slip instructions. Many MCQ screening tests have no negative marking, but always verify it for your specific test.

What is the passing marks for this test?

The official passing marks are generally 50%, but merit is competitive — scoring above 75% is a safer target to secure an interview call.

How can I prepare for the Data Scientist, Safety test?

Prepare with the free subject-wise MCQs, past papers and timed mock tests on PrepPro Academy — every question includes the answer with an explanation. Focus areas: Machine Learning, NLP, Safety Engineering, Data Analysis, Bias Detection.

Quick start

Review the job summary, then jump into prep with the same flow used for exam starts.

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