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Internship on the People Science Team at Palo Alto Networks, focused on building and validating LLM-driven workflows, integrating analytics data, and creating reporting frameworks.
Job Responsibility:
Build and validate LLM-driven workflows for producing behavioral assessments or work-sample tests, focusing on construct validity and measurement equivalence
Integrate people analytics data with unstructured text data to model drivers of role effectiveness using mixed-methods workforce research
Design and create parameterized, reproducible reporting frameworks to replace manual analytics reporting of our workforce data
Own and lead 1-2 discrete projects from conception to final delivery, scoped around your area of expertise
Requirements:
Currently enrolled in a PhD program (2nd year or later) in Industrial-Organizational Psychology, Organizational Behavior, Computational Social Science, Quantitative Psychology, or a related field
Strong foundations in measurement theory, research design, and an inferential framework (e.g., regression, multilevel modeling, psychometrics)
Fluency in Python or R for research workflows, automations, and statistical modeling or machine learning
A proven track record of translating complex people data into actionable insights (e.g., published research, whitepapers, or previous internships)
Proactive mindset with comfort navigating ambiguity and owning multiple projects
Nice to have:
Experience with computational text analysis, such as NLP, topic modeling, or working with large language models for qualitative coding