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hirly last saw it live on 30 September 2026. See similar open roles below, or browse all jobs in Washington, DC.
Amazon
Applied Scientist II, Amazon Supply Chain
Seattle, Washington, USA
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hirly's read of this role
- Role family
- Supply chain
- Seniority
- Mid level
- Country
- US
- Work mode
- On-site / unstated
- First seen by hirly
- 27 Sept 2026
Derived automatically from the posting.
the posting
As part of the AWS Applied AI Solutions organization, we have a vision to provide business applications, leveraging Amazon's unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers' businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. We blend vision with curiosity and Amazon's real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no-brainers to buy and easy to use.
We are looking for an Applied Scientist to join our team that is building enterprise applications leveraging machine learning, generative AI, and agentic AI to help millions of companies worldwide manage their day-to-day supply chain operations. Our mission is to accelerate our customers' businesses through intuitive, differentiated technology solutions that solve enduring supply chain challenges. We blend strategic vision with curiosity and Amazon's real-world operational experience to build opinionated, turnkey solutions that make the 'buy versus build' decision a no-brainer for our customers.
As an Applied Scientist, you will design and develop machine learning models and algorithms that power intelligent supply chain applications at global scale. You will work at the intersection of research and real-world product impact, translating scientific advances into production systems that serve millions of customers. We operate like a startup within AWS, offering you the opportunity to tackle complex challenges while working with the latest technologies in deep learning, large language models, and optimization.
If you are passionate about pushing the boundaries of applied science, thrive in ambiguous problem spaces, and want to shape the future of supply chain intelligence while having the backing of AWS's extensive resources, we want to hear from you.
- Key job responsibilities
- - Design, develop, and deploy machine learning models for demand forecasting, inventory optimization, anomaly detection, and supply chain decision-making.
- - Develop generative AI and agentic AI solutions that automate complex supply chain workflows and deliver intelligent, adaptive recommendations to customers.
- - Formulate real-world business problems as machine learning problems; define data requirements, model architectures, evaluation metrics, and experimentation frameworks.
- - Drive end-to-end applied science projects from ideation through experimentation, offline evaluation, A/B testing, and production deployment at scale.
- - Collaborate with engineering, product management, and business stakeholders to translate scientific capabilities into customer-facing product features, and mentor other scientists to raise the team's technical bar.
- A day in the life
- You will start many mornings reviewing experiment results and model metrics before joining a science sync where you and your teammates discuss progress, debug tricky modeling issues, and brainstorm new approaches. From there, you may spend focused time writing and testing model code in Python or PyTorch, running offline evaluations, or preparing an A/B test for a new forecasting algorithm. Expect regular working sessions with software engineers to integrate your models into production services, and occasional deep-dive reviews where you present your scientific approach and findings to the broader team.
- About the team
- The AWS Applied AI Solutions team builds enterprise applications that leverage Amazon's operational expertise to solve real-world supply chain challenges for millions of companies. We operate like a startup within AWS, moving fast and shipping iteratively with modern AI technologies. We invest in your growth through mentorship from experienced scientists, conference publication support, and internal science reading groups. If your career hasn't followed a traditional path, we encourage you to apply — we value varied experiences and perspectives.
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 programming in Java, C++, Python or related language
Preferred qualifications
- Experience using Unix/Linux
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.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits .
USA, WA, Seattle - 142,800.00 - 193,200.00 USD annually
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