Applied Scientist, ML Apps, People eXperience Technology Central Science (PXTCS)
Amazon.com
Do you want to use your expertise in translating innovative science into impactful products to improve the lives and work of over a million people worldwide? If you do, People eXperience Technology Central Science (PXTCS) would love to talk to you about how to 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 both improve Amazonian’s wellbeing and their ability to deliver value for Amazon’s customers. We work with HR teams across Amazon to make Amazon PXT the most scientific human resources organization in the world.
As an applied scientist on our team, you will work with business leaders, scientists, and economists to translate business and functional requirements into concrete deliverables, define the science vision and translate it into specific plans for applied scientists, as well as engineering and product teams. You will partner with scientists, economists, and engineers on the design, development, testing, and deployment of scalable ML and econometric models. This is a unique, high visibility opportunity for someone who wants to have impact, dive deep into large-scale solutions, enable measurable actions on the employee experience, and work closely with scientists and economists. This role combines science leadership, organizational ability, and technical strength.
Key job responsibilities
As an Applied Scientist, ML Applications, you will:
• Design, develop, and evaluate innovative machine learning solutions to solve diverse challenges and opportunities for Amazon customers
• Advance the team's engineering craftsmanship and drive continued scientific innovation as a thought leader and practitioner.
• Partner with the engineering team to deploy your models in production.
• Partner with scientists from across PXTCS to solve complex problems and use your team’s expertise to accelerate their ability get their work into production.
• Work directly with Amazonians from across the company to understand their business problems and help define and implement scalable ML solutions to solve them.
PXTCS is an interdisciplinary team that uses economics, behavioral science, statistics, and machine learning to identify products, mechanisms, and process improvements that both improve Amazonian’s wellbeing and their ability to deliver value for Amazon’s customers. We work with HR teams across Amazon to make Amazon PXT the most scientific human resources organization in the world.
As an applied scientist on our team, you will work with business leaders, scientists, and economists to translate business and functional requirements into concrete deliverables, define the science vision and translate it into specific plans for applied scientists, as well as engineering and product teams. You will partner with scientists, economists, and engineers on the design, development, testing, and deployment of scalable ML and econometric models. This is a unique, high visibility opportunity for someone who wants to have impact, dive deep into large-scale solutions, enable measurable actions on the employee experience, and work closely with scientists and economists. This role combines science leadership, organizational ability, and technical strength.
Key job responsibilities
As an Applied Scientist, ML Applications, you will:
• Design, develop, and evaluate innovative machine learning solutions to solve diverse challenges and opportunities for Amazon customers
• Advance the team's engineering craftsmanship and drive continued scientific innovation as a thought leader and practitioner.
• Partner with the engineering team to deploy your models in production.
• Partner with scientists from across PXTCS to solve complex problems and use your team’s expertise to accelerate their ability get their work into production.
• Work directly with Amazonians from across the company to understand their business problems and help define and implement scalable ML solutions to solve them.
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