Senior Applied Scientist, Agentic AI for Data Analytics and Science, Speed Analytics and Science
Job Description
Are you excited by the opportunity to define how Amazon builds and scales intelligent AI systems that influence strategic decisions across a massive transportation network? Do you want to lead the development of agentic AI solutions and mentor the next generation of scientists while shaping how we use generative AI at scale?Amazon’s North American Transportation Data Science & Analytics team is looking for a Senior Applied Scientist to lead the design and implementation of agentic AI systems that simulate, reason, and deliver insights across Amazon’s dynamic supply chain and delivery operations. You will set scientific direction, define development strategy, and lead by example—building intelligent agents that accelerate our ability to analyze complex data and make customer-centric decisions at scale.This is a high-impact, highly visible role. You'll collaborate closely with product, engineering, science, and business leaders across planning, forecasting, operations, and last-mile delivery. Delivery speed is one of Amazon’s core customer value propositions and a key differentiator—your work will directly shape how we measure and improve that experience.Key job responsibilities- Define the AI and agentic development strategy for transportation analytics, including use case prioritization, architecture principles, and integration frameworks- Lead the design and development of autonomous AI agents that deliver insights, simulate system behavior, and support experimentation across analytics workflows- Build systems that allow agents to interact with APIs, knowledge bases, structured and unstructured data sources- Mentor and develop junior scientists and engineers, providing scientific guidance, code reviews, and career development support- Collaborate with Data Scientists, BIEs, and Engineering teams to integrate agentic systems into production workflows and internal tools- Define and own agent evaluation frameworks—measuring business impact, correctness, and efficiency at scale- Drive adoption of shared infrastructure like reusable APIs, prompt libraries, and model registries- Partner with AWS teams and internal technical stakeholders to leverage emerging AI/ML technologies, and serve as an evangelist for scalable, interoperable solutions- Influence cross-org discussions on AI strategy, experimentation tooling, and reusable architecture to support Amazon-wide analytics goalsAbout the teamWe 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.
Locations
- United States, AZ, Tempe, Tempe, AZ, United States
- United States, WA, Bellevue, Bellevue, WA, United States
Salary
Salary not disclosed
Estimated Salary Rangehigh confidence
180,000 - 280,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- - 3+ years of building machine learning models for business application experienceintermediate
- - PhD, or Master's degree and 6+ years of applied research experienceintermediate
- - Experience programming in Java, C++, Python or related languageintermediate
- - Experience with neural deep learning methods and machine learningintermediate
Required Qualifications
- - 3+ years of building machine learning models for business application experience (experience, 3 years)
- - PhD, or Master's degree and 6+ years of applied research experience (experience, 6 years)
- - Experience programming in Java, C++, Python or related language (experience)
- - Experience with neural deep learning methods and machine learning (experience)
Preferred Qualifications
- - Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc. (experience)
- - Experience with large scale distributed systems such as Hadoop, Spark etc. (experience)
- Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/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. (experience)
Responsibilities
- - Define the AI and agentic development strategy for transportation analytics, including use case prioritization, architecture principles, and integration frameworks
- - Lead the design and development of autonomous AI agents that deliver insights, simulate system behavior, and support experimentation across analytics workflows
- - Build systems that allow agents to interact with APIs, knowledge bases, structured and unstructured data sources
- - Mentor and develop junior scientists and engineers, providing scientific guidance, code reviews, and career development support
- - Collaborate with Data Scientists, BIEs, and Engineering teams to integrate agentic systems into production workflows and internal tools
- - Define and own agent evaluation frameworks—measuring business impact, correctness, and efficiency at scale
- - Drive adoption of shared infrastructure like reusable APIs, prompt libraries, and model registries
- - Partner with AWS teams and internal technical stakeholders to leverage emerging AI/ML technologies, and serve as an evangelist for scalable, interoperable solutions
- - Influence cross-org discussions on AI strategy, experimentation tooling, and reusable architecture to support Amazon-wide analytics goals
- 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.
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Tags & Categories
Senior Applied Scientist, Agentic AI for Data Analytics and Science, Speed Analytics and Science
Job Description
Are you excited by the opportunity to define how Amazon builds and scales intelligent AI systems that influence strategic decisions across a massive transportation network? Do you want to lead the development of agentic AI solutions and mentor the next generation of scientists while shaping how we use generative AI at scale?Amazon’s North American Transportation Data Science & Analytics team is looking for a Senior Applied Scientist to lead the design and implementation of agentic AI systems that simulate, reason, and deliver insights across Amazon’s dynamic supply chain and delivery operations. You will set scientific direction, define development strategy, and lead by example—building intelligent agents that accelerate our ability to analyze complex data and make customer-centric decisions at scale.This is a high-impact, highly visible role. You'll collaborate closely with product, engineering, science, and business leaders across planning, forecasting, operations, and last-mile delivery. Delivery speed is one of Amazon’s core customer value propositions and a key differentiator—your work will directly shape how we measure and improve that experience.Key job responsibilities- Define the AI and agentic development strategy for transportation analytics, including use case prioritization, architecture principles, and integration frameworks- Lead the design and development of autonomous AI agents that deliver insights, simulate system behavior, and support experimentation across analytics workflows- Build systems that allow agents to interact with APIs, knowledge bases, structured and unstructured data sources- Mentor and develop junior scientists and engineers, providing scientific guidance, code reviews, and career development support- Collaborate with Data Scientists, BIEs, and Engineering teams to integrate agentic systems into production workflows and internal tools- Define and own agent evaluation frameworks—measuring business impact, correctness, and efficiency at scale- Drive adoption of shared infrastructure like reusable APIs, prompt libraries, and model registries- Partner with AWS teams and internal technical stakeholders to leverage emerging AI/ML technologies, and serve as an evangelist for scalable, interoperable solutions- Influence cross-org discussions on AI strategy, experimentation tooling, and reusable architecture to support Amazon-wide analytics goalsAbout the teamWe 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.
Locations
- United States, AZ, Tempe, Tempe, AZ, United States
- United States, WA, Bellevue, Bellevue, WA, United States
Salary
Salary not disclosed
Estimated Salary Rangehigh confidence
180,000 - 280,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- - 3+ years of building machine learning models for business application experienceintermediate
- - PhD, or Master's degree and 6+ years of applied research experienceintermediate
- - Experience programming in Java, C++, Python or related languageintermediate
- - Experience with neural deep learning methods and machine learningintermediate
Required Qualifications
- - 3+ years of building machine learning models for business application experience (experience, 3 years)
- - PhD, or Master's degree and 6+ years of applied research experience (experience, 6 years)
- - Experience programming in Java, C++, Python or related language (experience)
- - Experience with neural deep learning methods and machine learning (experience)
Preferred Qualifications
- - Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc. (experience)
- - Experience with large scale distributed systems such as Hadoop, Spark etc. (experience)
- Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/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. (experience)
Responsibilities
- - Define the AI and agentic development strategy for transportation analytics, including use case prioritization, architecture principles, and integration frameworks
- - Lead the design and development of autonomous AI agents that deliver insights, simulate system behavior, and support experimentation across analytics workflows
- - Build systems that allow agents to interact with APIs, knowledge bases, structured and unstructured data sources
- - Mentor and develop junior scientists and engineers, providing scientific guidance, code reviews, and career development support
- - Collaborate with Data Scientists, BIEs, and Engineering teams to integrate agentic systems into production workflows and internal tools
- - Define and own agent evaluation frameworks—measuring business impact, correctness, and efficiency at scale
- - Drive adoption of shared infrastructure like reusable APIs, prompt libraries, and model registries
- - Partner with AWS teams and internal technical stakeholders to leverage emerging AI/ML technologies, and serve as an evangelist for scalable, interoperable solutions
- - Influence cross-org discussions on AI strategy, experimentation tooling, and reusable architecture to support Amazon-wide analytics goals
- 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.