Applied Scientist, PXT Central Science
Listed on Amazon’s official careers site. Posted October 1, 2026. Check that it is still open and apply there.
Apply on Amazon’s careers site →Job description
The People eXperience Technology (PXT) Central Science (PXTCS)'s mission is to make PXT the most scientific, technologically proficient, and inclusive HR organization in the world. PXTCS does this by accelerating scientific rigor in business-led initiatives, working backwards from employee experience, and delivering science-driven products that improve the well-being of and value of work for Amazonians worldwide.
We are seeking an Applied Scientist to build production machine learning systems that solve complex business problems at scale. You will design, develop, and deploy ML solutions that directly impact millions of users and drive strategic decision-making across the organization. In this role, you will work on challenging problems spanning predictive modeling, computer vision, natural language processing, recommendation systems, and generative AI applications.
You will collaborate with cross-functional teams to translate ambiguous business challenges into rigorous technical solutions.
Key job responsibilities
- Apply and adapt state-of-the-art scientific techniques to solve well-defined problems in employee experience, using reasonable assumptions, data, and customer requirements.
- Design, develop, and implement small-to-medium ML components with input and guidance from senior scientists, taking ownership of the code in your components.
- Write secure, stable, testable, maintainable, well-reviewed code (at the SDE I bar) to deliver solutions into production that benefit customers and the business.
- Collaborate with cross-functional partners to understand business context and impact, and help mentor interns.
- Communicate complex technical concepts to diverse audiences, from technical peers to senior leadership
About the team
The People eXperience and Technology Central Science Team (PXTCS) uses economics, applied science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal.
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