Senior Applied Scientist , Research and Applied Science Team, PXT Senior Talent and Transformation

Key job responsibilities

• Own the production implementation of the team's scientific systems from end to end.
• Translate validated methodologies into code that runs reliably, scales, and does not require a scientist standing next to it.
• Make architectural and tooling decisions that determine how scientific methods get encoded into software.
• Define and hold the engineering quality bar for scientific code across the team.
• Build LLM-powered pipelines that operationalize the team's people science.
• Extend and adapt scientific techniques at the product level when established approaches fall short.
• Contribute to the design and execution of quasi-experimental evaluations of people programs.
• Mentor scientists on the team on software engineering practices and applied implementation.
• Communicate implementation trade-offs and system design decisions clearly to product and HR partners.

Basic Qualifications

• Experience leading the architecture and design of new and current systems, or experience building complex software systems.
• PhD in industrial-organizational psychology, organizational behavior, economics, statistics, computer science, or a related quantitative discipline.
• 5+ years of applied research experience after the PhD, with a demonstrable track record of delivering scientifically complex solutions into production systems.
• Strong software engineering skills in Python, including the ability to design, build, test, and maintain production pipelines independently.
• Deep scientific expertise in at least one of the following areas and enough working knowledge in the others to contribute meaningfully across the team's full research portfolio: psychometric measurement and validation, causal inference with observational and quasi-experimental data, or applied LLM systems including prompt orchestration and evaluation.

Prioritization of Qualifications

• Experience serving as the primary or sole implementer of scientific systems on a research team.
• Experience building LLM pipelines including retrieval-augmented generation, automated scoring, and LLM-as-judge evaluation harnesses.
• Applied experience with quasi-experimental methods such as difference-in-differences, regression discontinuity, matching, or synthetic control.
• Experience establishing and modeling software engineering best practices, such as testing, documentation, and code review.