Senior Applied Scientist , Research and Applied Science Team, PXT Senior Talent and Transformation
About the role
The Senior Talent and Transformation Science team within Amazon's People eXperience and Technology organization works on developing systems that shape how Amazon identifies, develops, and invests in its most senior leaders. This role involves translating validated scientific methodologies into deployable systems that can be used by any HR team across a company of over a million employees.
Responsibilities
Own the production implementation of the team's scientific systems from end to end.
Translate validated scientific methodologies into code that runs reliably, scales, and does not require a scientist standing next to it to operate.
Make architectural and tooling decisions that determine how scientific methods get encoded into software, choosing abstractions, data structures, and system designs that make the team's scientific components testable, maintainable, and extensible over time.
Define and hold the engineering quality bar for scientific code across the team, establishing and modeling best practices for testing, documentation, reproducibility, and peer review of code in a research team that does not have dedicated software development engineers.
Build LLM-powered pipelines that operationalize the team's people science, including prompt orchestration, retrieval grounding, automated scoring, and LLM-as-judge evaluation harnesses, writing the implementation yourself and owning the quality and reliability of those systems once deployed.
Extend and adapt scientific techniques at the product level when established approaches fall short. Devise and implement solutions for methodological challenges that require rigorous scientific rigor within real production constraints.
Partner with the Research Scientists during methodology design to surface implementation feasibility and trade-offs early, contributing your own scientific judgment on what can be built rigorously within real production constraints before design decisions become expensive to reverse.
Build reusable scientific components, services, and templates that encode methodology once and allow downstream teams to run it without scientist involvement, making the team's research operational infrastructure rather than a bespoke consulting engagement.
Contribute to the design and execution of quasi-experimental evaluations of people programs, owning the analytical implementation and the code pipelines that produce defensible causal evidence from observational and field data.
Mentor scientists on the team on software engineering practices and applied implementation, and participate actively in peer review of experiment designs, analytical approaches, and scientific code written by others.
Communicate implementation trade-offs and system design decisions clearly to product and HR partners in written documents that connect technical choices to business outcomes.
Qualifications
Experience leading the architecture and design of new and current systems, or experience building complex software systems that have been successfully delivered to customers.
PhD in industrial-organizational psychology, organizational behavior, economics, statistics, computer science, or a related quantitative discipline with 5+ years of applied research experience after the PhD, with a demonstrable track record of delivering scientifically complex solutions into production systems that other teams depend on.
Strong software engineering skills in Python, including the ability to design, build, test, and maintain production pipelines independently without dedicated software development engineering support.
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.
Preferred Qualifications
Experience serving as the primary or sole implementer of scientific systems on a research team, where engineering quality and production reliability were your responsibility rather than a dedicated engineer's.
Hands-on experience building LLM pipelines including retrieval-augmented generation, automated scoring, and LLM-as-judge evaluation harnesses, with direct ownership of those systems in production.
Applied experience with quasi-experimental methods such as difference-in-differences, regression discontinuity, matching, or synthetic control in field settings where identification strategy required genuine methodological judgment rather than textbook application.
Experience establishing and modeling software engineering best practices, such as testing, documentation, and code review, for colleagues who are strong scientists but not trained software engineers.
Publications or presentations at venues such as SIOP, AOM, NeurIPS, EMNLP, or peer-reviewed journals in measurement, causal inference, or machine learning.