Description
As a People Data Scientist, you will bring deep expertise in research science, measurement, and experimentation to OpenAI's most important People programs. You will design studies, evaluate people processes, build research frameworks, and help leaders understand how we can better empower employees, strengthen organisational systems, and deliver exceptional employee experiences.
We're looking for an experienced data scientist who can translate ambiguous People questions into rigorous research designs, validated insights, and actionable recommendations.
This role will be based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.
Key Responsibilities:
- Design rigorous research and evaluation strategies for organisational health, manager effectiveness, employee experience, and talent outcomes.
- Conduct fairness, adverse impact, validity, reliability, calibration, and measurement-invariance analyses for high-stakes People processes and AI-assisted workflows.
- Apply advanced statistical modeling, machine learning, and research methods to inform program design, evaluate effectiveness, and quantify business impact.
- Partner with People Operations, data engineering, and people systems teams to define data requirements, improve data quality, establish documentation standards, and ensure research datasets are governed, reproducible, and privacy-preserving.
- Build scalable people science infrastructure, including self-service agentic tools, automated validation workflows, reusable research datasets and analytical pipelines.
- Develop research playbooks that establish rigorous standards for study design, measurement, validation, and documentation, enabling high-quality, repeatable, and scalable research across the organisation.
- Communicate findings through concise, executive-ready narratives.
Requirements:
- Deep curiosity, strong attention to detail, and passion for solving ambiguous and complex problems with creativity.
- Exceptional strength in research design, experimentation, measurement, causal inference, and statistical modeling, including hands-on experience with psychometrics, survey methodology, structural equation modeling, multilevel modeling, randomised controlled experiments, A/B testing, quasi-experimental design, validation studies, and machine learning evaluation.
- High proficiency in R or Python and SQL, with experience working across 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 trade-offs clearly to senior leaders, technical partners, and non-technical audiences.
Preferred Qualifications:
- Experience evaluating AI-assisted workflows, algorithmic systems, and human-AI decision processes in operational contexts, including familiarity with model evaluation methods.
- Advanced degree in Industrial-Organisational Psychology, Organisational Behaviour, Quantitative Psychology, Behavioural Economics, Statistics, Economics, Data Science, or a related field; PhD preferred.