Backed by Greylock, Insight and Khosla.
About the role
As a People Data Scientist focused on AI fairness and bias testing, you will help establish how OpenAI evaluates AI-assisted People systems and high-impact talent processes. You will design and conduct rigorous assessments to identify, measure, and mitigate potential bias across the lifecycle of models, agents, decision-support tools, and automated workflows.
What they're looking for
- Deep expertise in algorithmic fairness, bias measurement, responsible AI, psychometrics, applied statistics, or the evaluation of high-impact decision systems
- Exceptional strength in research design, measurement, experimentation, causal inference, and statistical modeling
- Strong judgment about the limitations of fairness metrics, including the ability to determine which measures are appropriate for a particular decision context rather than applying a single universal definition of fairness
- Experience evaluating machine-learning models, generative AI systems, agents, or human-AI workflows using quantitative and qualitative evidence
- High proficiency in Python or R and SQL, with experience working across complex, sensitive, and imperfect datasets
- Experience building reproducible evaluation pipelines, automated testing frameworks, analytical tools, monitoring systems, or governed research workflows
More about this role
OpenAI’s People team hires, engages, and retains world-class talent to safely build and deploy AGI that benefits all of humanity. The People Analytics team helps leaders make rigorous, evidence-based talent decisions and ensures that the systems supporting those decisions are valid, reliable, fair, and accountable.
As a People Data Scientist focused on AI fairness and bias testing, you will help establish how OpenAI evaluates AI-assisted People systems and high-impact talent processes. You will design and conduct rigorous assessments to identify, measure, and mitigate potential bias across the lifecycle of models, agents, decision-support tools, and automated workflows.
Your work will span the entire employee life-cycle, such as hiring, performance, promotion, employee development, workforce planning, etc. You will evaluate both technical systems and the broader human-AI decision processes in which they operate, examining not only model performance but also data quality, measurement validity, differential outcomes, human oversight, and unintended consequences.
We’re looking for an experienced data scientist or applied researcher who can translate complex fairness questions into...
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