People Research Data Scientist, AI Fairness & Bias

About the Role

As a key member of the People Analytics team, the People Research Data Scientist will be responsible for establishing and leading fairness and bias-testing strategies for AI-assisted People processes. This will involve designing and conducting algorithmic audits and validation studies to identify, measure, and mitigate potential bias across the lifecycle of models, agents, decision-support tools, and automated workflows. The role requires a deep understanding of AI fairness, bias measurement, and responsible AI, as well as exceptional strength in research design, measurement, experimentation, causal inference, and statistical modeling.

The People Research Data Scientist will be based in San Francisco, CA, and will work closely with the engineering, People Operations, Legal, Privacy, Security, and People Systems teams to recommend and evaluate mitigations such as data improvements, model changes, threshold adjustments, workflow redesign, monitoring controls, and additional human oversight.

What You Will Do

• Define and lead fairness and bias-testing strategies for AI-assisted People processes, models, agents, and decision-support systems from development through deployment and ongoing monitoring.
• 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 the appropriate fairness criteria for each use case, evaluate tradeoffs among competing definitions of fairness, and clearly document the assumptions, limitations, and residual risks of each approach.
• 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.
• Investigate the sources of observed disparities, including data representation, label and measurement bias, proxy variables, model design, decision thresholds, workflow design, and differential adoption or usage.
• Partner with engineering, People Operations, Legal, Privacy, Security, and People Systems teams to recommend and evaluate mitigations such as data improvements, model changes, threshold adjustments, workflow redesign, monitoring controls, and additional human oversight.
• Build scalable fairness-evaluation infrastructure, including reusable datasets, automated validation pipelines, regression tests, monitoring systems, self-service tools, and standardized reporting.
• Establish research and documentation standards for fairness test plans, dataset and model documentation, validation reports, limitations, monitoring plans, and decision records.
• Translate complex findings into concise, decision-ready narratives, helping leaders understand the significance of identified risks, the strength of the evidence, available mitigation options, and remaining uncertainty.

What We Are 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.
• Hands-on experience with data science tools and technologies, including programming languages such as Python, R, or Julia.
• Strong understanding of machine learning and AI systems, including model development, deployment, and monitoring.
• Experience working with large datasets, including data preprocessing, feature engineering, and data visualization.
• Excellent communication and collaboration skills, with the ability to work effectively with cross-functional teams.
• Strong problem-solving skills, with the ability to analyze complex problems and develop creative solutions.
• Experience working in a fast-paced, dynamic environment, with the ability to adapt to changing priorities and deadlines.

Nice to Have

• Experience working in the field of AI fairness and bias, with a deep understanding of the technical and social challenges involved.
• Knowledge of regulatory requirements and industry standards related to AI fairness and bias.
• Experience working with cloud-based data platforms, including AWS, GCP, or Azure.
• Familiarity with agile development methodologies, including Scrum or Kanban.
• Experience working with remote teams, with the ability to collaborate effectively in a distributed environment.

Benefits and Perks

• Competitive salary and equity package.
• Comprehensive health, dental, and vision insurance.
• Generous PTO and sick leave policy.
• Flexible work arrangements, including remote work options.
• Professional development opportunities, including training and conference sponsorships.
• Access to cutting-edge technology and tools.
• Collaborative and dynamic work environment.
• Opportunity to work on high-impact projects that can make a real difference in the world.

• When applying for this role, be sure to highlight your experience working with data science tools and technologies, including programming languages such as Python, R, or Julia.
• Showcase your understanding of machine learning and AI systems, including model development, deployment, and monitoring.
• Emphasize your ability to work effectively in a cross-functional team environment, including experience working with engineering, People Operations, Legal, Privacy, Security, and People Systems teams.
• Be prepared to discuss your approach to ensuring fairness and mitigating bias in AI systems, including your experience with algorithmic audits and validation studies.
• When negotiating salary, be sure to research the market rate for similar roles and be prepared to make a strong case for your worth.
• Don't be afraid to ask about the company culture and values, including their approach to diversity and inclusion.