People Research Scientist
Conduct rigorous people research and applied data science to evaluate talent programs, organizational health, and employee experiences. The role requires advanced expertise in research design, experimentation, measurement, causal inference, statistical modeling, and responsible handling of sensitive employee data.
About the job
Responsibilities
- Design rigorous research and evaluation strategies for recruiting, organizational health, manager effectiveness, employee experience, and talent outcomes.
- Apply statistical modeling, machine learning, causal inference, experimentation, and research methods to inform program design and quantify impact.
- Partner with People Operations, data engineering, and people systems teams to define data requirements, improve data quality, and establish governed, reproducible, privacy-preserving research datasets.
- Build scalable people science infrastructure, including self-service agentic tools, automated validation workflows, reusable datasets, and analytical pipelines.
- Develop research playbooks covering study design, measurement, validation, and documentation.
- Communicate findings through concise, executive-ready narratives.
Requirements
- Strong expertise in research design, experimentation, measurement, causal inference, and statistical modeling.
- Hands-on experience with psychometrics, survey methodology, structural equation modeling, multilevel modeling, randomized controlled experiments, A/B testing, quasi-experimental design, validation studies, and machine learning evaluation.
- High proficiency in R or Python and SQL, working with complex, messy datasets.
- Experience building measurement systems, research programs, data products, reusable analytics frameworks, self-service tools, and governed analytical workflows.
- Ability to communicate complex methods and tradeoffs to senior leaders, technical partners, and non-technical audiences.
- Sound judgment when handling sensitive employee data, including privacy, fairness, bias, and responsible research practices.
Nice-to-haves
- Experience evaluating AI-assisted workflows, algorithmic systems, and human-AI decision processes in operational contexts.
- Familiarity with model evaluation methods.
- Advanced degree in Industrial-Organizational Psychology, Organizational Behavior, Quantitative Psychology, Behavioral Economics, Statistics, Economics, Data Science, or a related field.
Compensation
- Salary: $198,000–$220,000
Skills
R, Python, SQL, Machine Learning, Causal Inference, Statistical Modeling, Psychometrics, Survey Methodology, Structural Equation Modeling, Multilevel Modeling, A/B Testing, Randomized Controlled Experiments, Quasi-Experimental Design, Machine Learning Evaluation, Data Pipelines
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