People Research Data Scientist (AI Fairness & Bias)

TL;DR

People Research Data Scientist (AI Fairness & Bias): Developing and leading evaluation strategies to identify and mitigate bias in AI-assisted People systems with an accent on algorithmic auditing and psychometric validation. Focus on designing rigorous assessments for generative AI, analyzing differential outcomes in talent processes, and building scalable fairness-evaluation infrastructure.

Location: Preferred to be based in San Francisco, CA; other mentioned hubs include New York City, Seattle, and Washington, DC.

Salary: $198K – $220K + Equity

Company

hirify.global is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity.

What you will do

• Lead fairness and bias-testing strategies for AI-assisted People processes, models, and decision-support systems.
• Design rigorous algorithmic audits, including adverse-impact analysis, subgroup evaluation, and calibration testing.
• Evaluate end-to-end human-AI decision systems, examining model outputs, user behavior, and the equity of outcomes.
• Develop evaluation approaches for generative and agentic AI, incorporating counterfactual testing and human-rating studies.
• Build scalable fairness-evaluation infrastructure, including automated validation pipelines and monitoring systems.
• Partner with Engineering, Legal, Privacy, and Security teams to recommend and evaluate bias mitigations.

Requirements

• Deep expertise in algorithmic fairness, bias measurement, responsible AI, psychometrics, or applied statistics.
• Exceptional strength in research design, measurement, experimentation, and causal inference.
• High proficiency in Python or R and SQL, with experience handling complex and sensitive datasets.
• Experience evaluating machine-learning models, generative AI systems, or human-AI workflows.
• Advanced degree in Quantitative Psychology, Computer Science, Statistics, Economics, or Data Science (PhD preferred).
• Must be based in the US (Preferred location: San Francisco, CA).

Nice to have

• Familiarity with tools like Fairlearn, AI Fairness 360, or other responsible-AI evaluation frameworks.
• Experience evaluating LLMs, safety classifiers, or agentic workflows via behavioral testing.
• Background in employment selection, talent assessment, or organizational research.
• Experience creating model cards, fairness scorecards, and audit reports for high-impact systems.

Culture & Benefits

• Opportunity to contribute to the safety and fairness of AGI development.
• Collaborative environment working with world-class researchers and engineers.
• Competitive compensation package including equity.
• Commitment to diversity, equity, and inclusion as an equal opportunity employer.