Sr. Applied Scientist, Topline Forecasting

Job title: Sr. Applied Scientist, Topline Forecasting

Company: Amazon


Job description: DESCRIPTIONWhere will Amazon’s growth come from in the next year? What about over the next five? What are the drivers and factors of such growth? Are we investing enough in our infrastructure, or too much? How do our customers react to changes in prices, product selection, or delivery times? These are among the most important questions at Amazon today. The Forecasting team in the Supply Chain Optimization Technologies (SCOT) organization is dedicated to answering these questions using quantitative and statistical methods. We build accurate predictive models and deploy automated software solutions to provide forecasting insights to business leaders at the most senior levels throughout the company.As a Sr. Applied Scientist, you will work with economists, software engineers, product managers, and business teams to understand the business problems and requirements, distill that understanding to crisply define patterns of problems, and design and develop innovative solutions to systematically address them. Our team is highly cross-functional and employs a wide array of scientific tools and techniques to solve key challenges, including large scale state-space models, large language models, linear, causal inference, and optimization.Key job responsibilities

• Design, implement, and evaluate innovative models, agents, and software prototypes.
• Collaborate with a team of experienced scientists to drive technological advancements.
• Develop innovative solutions to complex business problems in collaboration with partner teams.
• Work with engineering teams and product managers to develop new tools and systems to support and scale the growth of the business
• Contribute to Amazon’s global science community through collaboration and publication of ground-breaking research.
• Engage in research projects that contribute to the wider scientific community, sharing findings through publications in top-tier journals and conferences.
• Mentor and develop the scientist community across the organization

BASIC QUALIFICATIONS– 3+ years of building machine learning models for business application experience
– PhD, or Master’s degree and 6+ years of applied research experience
– Experience programming in Java, C++, Python or related languagePREFERRED QUALIFICATIONS– Experience with large scale distributed systems such as Hadoop, Spark etc.
– PhD in econometrics, statistics, industrial engineering, operations research, optimization, data mining, analytics, or equivalent quantitative field
– Experience with conducting research in a corporate settingAmazon 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 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 $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 . This position will remain posted until filled. Applicants should apply via our internal or external career site.

Expected salary: $150400 per year

Location: Bellevue, WA

Job date: Sun, 18 May 2025 04:10:05 GMT

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