DESCRIPTION

Are you passionate about building intelligent AI systems that deliver insights and shape strategic decisions at scale? Do you want to redefine how analytics is done by leveraging generative AI and autonomous agents?
Amazon’s North American Transportation Data Science & Analytics team is looking for an exceptional Applied Scientist to lead the development of agentic AI solutions that simulate, reason, and deliver insights across our complex transportation network. You will build intelligent agents that act as data analysts—identifying patterns, generating hypotheses, and accelerating decision-making for high-impact logistics and delivery challenges.
This is a high-impact role that combines deep scientific thinking with practical system building. You’ll work closely with product, engineering, and science teams across domains—including planning, forecasting, supply chain, and operations—to shape the future of how Amazon analyzes and improves delivery speed to customers. Delivery speed is one of Amazon’s key differentiators and a critical driver of customer satisfaction. This is a fast-moving, customer-obsessed space where the work you do will have direct and measurable impact on the customer experience across North America.

Key job responsibilities

• Design, build, and deploy autonomous AI agents that support advanced analytics and experimentation workflows in transportation.
• Develop agents that can interact with APIs, query internal data/knowledge sources, and reason over structured and unstructured data.
• Collaborate with Data Scientists, BIEs, and Engineers to integrate agentic systems into existing analytical pipelines and business tools.
• Use agentic AI to simulate customer or system behavior and define new performance and quality metrics for delivery experience.
• Develop evaluation frameworks to measure agent performance, scalability, and business impact.
• Contribute to the development of shared infrastructure for agent development—such as reusable APIs, prompt libraries, and model registries.
• Partner with internal AWS teams to stay aligned with best-in-class tools and infrastructure for generative and agentic AI.
• Stay current on emerging AI/ML technologies and act as an evangelist within the team—driving adoption of new approaches and ensuring scalable, modern solutions are leveraged where appropriate.
• Guide quality improvement programs through data-driven insights generated by agentic systems.

About the team
We enable data-driven decision-making for Amazon’s North American Transportation network by combining deep analytics, causal modeling, and experimentation. Our team supports business and tech stakeholders across planning, delivery, and customer experience by developing scalable tools, dashboards, and metrics. As we evolve toward agentic and generative AI, we are reimagining how insights are produced—moving from static reports to intelligent, autonomous systems that proactively surface opportunities and drive operational excellence.

BASIC QUALIFICATIONS

• 3+ years of building models for business application experience
• PhD, or Master’s degree and 4+ years of CS, CE, ML or related field experience
• Experience in patents or publications at top-tier peer-reviewed conferences or journals
• Experience programming in Java, C++, Python or related language
• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

PREFERRED QUALIFICATIONS

• Experience using Unix/Linux
• Experience in professional software development
  Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
  Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
  Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $223,400/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site

• Design, build, and deploy autonomous AI agents that support advanced analytics and experimentation workflows in transportation.
• Develop agents that can interact with APIs, query internal data/knowledge sources, and reason over structured and unstructured data.
• Collaborate with Data Scientists, BIEs, and Engineers to integrate agentic systems into existing analytical pipelines and business tools.
• Use agentic AI to simulate customer or system behavior and define new performance and quality metrics for delivery experience.
• Develop evaluation frameworks to measure agent performance, scalability, and business impact.
• Contribute to the development of shared infrastructure for agent development—such as reusable APIs, prompt libraries, and model registries.
• Partner with internal AWS teams to stay aligned with best-in-class tools and infrastructure for generative and agentic AI.
• Stay current on emerging AI/ML technologies and act as an evangelist within the team—driving adoption of new approaches and ensuring scalable, modern solutions are leveraged where appropriate.
• Guide quality improvement programs through data-driven insights generated by agentic systems