Applied Science Manager, People eXperience Technology Central Science (PXTCS)
Amazon.com
Do you want to leverage your expertise in translating innovative science into impactful products to improve the lives and work of over a million people worldwide? If so, People eXperience Technology Central Science (PXTCS) would love to discuss how you can make that a reality.
PXTCS is an interdisciplinary team that uses economics, behavioral science, statistics, and machine learning to identify products, mechanisms, and process improvements that enhance Amazonians' well-being and their ability to deliver value for Amazon's customers. We collaborate with HR teams across Amazon to make Amazon PXT the most scientific human resources organization in the world.
This is a customer-facing science leadership role at PXTCS. As an Applied Science Manager, you will provide scientific thought leadership to PXT and other Amazon partners in AI/ML domains like NLP, LLM, and predictive analysis. You will play a critical role in business planning, working closely with senior leaders to develop goals, resource requirements, and influence technical and business strategies. Additionally, you will hire and develop science and engineering talent.
Key job responsibilities
Your responsibilities include collaborating with business leaders, scientists, UX researchers, and economists to translate requirements into concrete deliverables, define the science vision, and develop specific plans for teams. You will lead on the design, development, testing, and deployment of scalable ML and econometric models.
This is a unique, high-visibility opportunity to have a measurable impact on Amazon and its employees while working closely with business leaders, scientists and engineers. The role combines customer engagement, science leadership, management and organizational abilities, and technical strength.
Key Responsibilities:
• Interact directly with internal customers across Amazon to understand HR business problems, identify AI/ML solutions, and define projects and success criteria.
• Lead a team of scientists and engineers to rapidly design, prototype, and productionize scalable AI/ML solutions.
• Employ a metric-driven approach to evaluate customer-facing engagements.
PXTCS is an interdisciplinary team that uses economics, behavioral science, statistics, and machine learning to identify products, mechanisms, and process improvements that enhance Amazonians' well-being and their ability to deliver value for Amazon's customers. We collaborate with HR teams across Amazon to make Amazon PXT the most scientific human resources organization in the world.
This is a customer-facing science leadership role at PXTCS. As an Applied Science Manager, you will provide scientific thought leadership to PXT and other Amazon partners in AI/ML domains like NLP, LLM, and predictive analysis. You will play a critical role in business planning, working closely with senior leaders to develop goals, resource requirements, and influence technical and business strategies. Additionally, you will hire and develop science and engineering talent.
Key job responsibilities
Your responsibilities include collaborating with business leaders, scientists, UX researchers, and economists to translate requirements into concrete deliverables, define the science vision, and develop specific plans for teams. You will lead on the design, development, testing, and deployment of scalable ML and econometric models.
This is a unique, high-visibility opportunity to have a measurable impact on Amazon and its employees while working closely with business leaders, scientists and engineers. The role combines customer engagement, science leadership, management and organizational abilities, and technical strength.
Key Responsibilities:
• Interact directly with internal customers across Amazon to understand HR business problems, identify AI/ML solutions, and define projects and success criteria.
• Lead a team of scientists and engineers to rapidly design, prototype, and productionize scalable AI/ML solutions.
• Employ a metric-driven approach to evaluate customer-facing engagements.
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