People Research Data Scientist, AI Fairness & Bias

Tech Stack

Responsibilities

• Define and lead fairness and bias-testing strategies for AI-assisted People processes, models, agents, and decision-support systems.
• Design rigorous algorithmic audits and validation studies, including adverse-impact analysis, subgroup and intersectional evaluation, error-rate analysis, calibration, measurement invariance, reliability, criterion-related validity, and sensitivity testing.
• Identify appropriate fairness criteria for each use case, evaluate tradeoffs among competing definitions of fairness, and clearly document assumptions, limitations, and residual risks.
• Evaluate end-to-end human-AI decision systems, including model outputs, user behavior, human overrides, escalation pathways, and whether AI assistance changes the quality, consistency, or equity of decisions.
• Develop evaluation approaches for generative and agentic AI, including test-set design, counterfactual testing, behavioral evaluation, human-rating studies, robustness testing, and analysis of disparate performance across populations and contexts.

Benefits

• Equity

Culture

Requirements