Are you passionate about solving complex problems that impact millions of customers? Join Amazon's Supply Chain Optimization Technologies (SCOT) Long-Term Planning team, where you will architect the future of the world's most advanced fulfillment network.
As an Applied Scientist on our team, you will drive multi-billion dollar investment decisions that shape Amazon's global fulfillment strategy. You will pioneer innovative solutions using operations research, machine learning and statistical techniques, directly influencing Amazon's ability to delight customers while optimizing billions in capital investments. Your insights and recommendations will be presented to the highest levels of senior leadership, helping guide company-wide strategy.
In this role, you will develop our next-generation Multi-Tier Marginal Benefit Analysis (MT-MBA) platform. This involves creating sophisticated forecasting models that power network expansion decisions, transforming complex supply chain challenges into elegant mathematical solutions. You will design and implement novel algorithms that optimize warehouse placement, timing, and capacity, all while leveraging advanced machine learning techniques and large-scale optimization modeling.
You will join our Long-Term Planning Organization within SCOT, working alongside world-class applied scientists, economists, research scientists, product managers, and software engineers. We are seeking innovative thinkers who can balance theoretical excellence with practical implementation. Your ability to communicate complex technical concepts and drive data-driven decisions at massive scale will be crucial to your success.
- PhD, or Master's degree and 5+ years of building machine learning models or developing algorithms for business application experience
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- 3+ years of science, technology, engineering or related field experience
- Experience programming in Java, C++, Python or related language- Experience in professional software development
- Demonstrated research experience in Causal Inference, Bayesian Modeling, or Optimization with a proven track record of publications at well-regarded conferences and journals
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.
