Stores Economics and Science (SEAS) is an interdisciplinary science and engineering team in Amazon's Stores organization with a peak-jumping mission: we apply expertise in science and engineering to move from local to global optima in methods, models, and software. We pursue this mission by leveraging frontier science; collaborating with partner teams; and learning from the tools, experience, and perspective of others. We scale by solving problems, first in the small to prove concepts, and then ... more details
DESCRIPTION
Stores Economics and Science (SEAS) is an interdisciplinary science and engineering team in Amazon's Stores organization with a peak-jumping mission: we apply expertise in science and engineering to move from local to global optima in methods, models, and software. We pursue this mission by leveraging frontier science; collaborating with partner teams; and learning from the tools, experience, and perspective of others. We scale by solving problems, first in the small to prove concepts, and then in the large by building scalable solutions. We also help other teams within Amazon scale by hiring and developing the best and embedding them in other business units. In 2024, we are focused on economics and science in areas related to (1) improving delivery speed and lowering cost-to-serve, (2) seller fees and incentives, and (3) emerging machine learning. We also have some ongoing and highly-leveraged collaborations that help partner teams inside Amazon short-circuit months of R&D or otherwise look around corners. We are looking for an Applied Scientist to build and deliver cutting-edge science and engineering solutions to improve our Stores business. In this role, you will work in a team of scientists and engineers with backgrounds in machine learning, NLP, IR, statistics, and economics to identify bottlenecks in our business, conceive new ideas to overcome those challenges, and deploy scientific solutions in partnership with product teams. Your responsibilities include developing and maintaining the scientific models, benchmarks, and services. Graduate education or hands-on experience in machine learning, optimization, causal inference, Bayesian statistics, deep learning, or other quantitative scientific fields is a big plus. To be successful in this role, you should be a quick learner and comfortable with a high degree of ambiguity.
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 committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
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 $222,200/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.
Job Abstracts is an independent Job Search Engine. Job Abstracts is not an agent or representative and is not endorsed, sponsored or affiliated with any employer. Job Abstracts uses proprietary technology to keep the availability and accuracy of its job listings and their details. All trademarks, service marks, logos, domain names, and job descriptions are the property of their respective holder. Job Abstracts does not have its members apply for a job on the jobabstracts.com website. Additionally, Job Abstracts may provide a list of third-party job listings that may not be affiliated with any employer. Please make sure you understand and agree to the website's Terms & Conditions and Privacy Policies you are applying on as they may differ from ours and are not in our control.
We would like to take a second to Welcome You to Job Abstracts, the nation’s largest Pure Job Board. With over 3.1 million job listings from 15,000+ Companies & Organizations, we help job searchers find careers that match their interests. As an anonymous user, you have probably discovered how easy our system is to use. However, you have just scratched the surface of what we can offer.
We encourage you to Register so you can use our most powerful features: searching with multiple terms, setting up multiple locations, establishing favorite companies, and accessing your search history. If you find a job you like, you can apply directly for it, and then, keep notes on it. We will also keep a lookout for jobs that match your search terms and email you when we find something you may like.
You can register for free and the system is free to use. If you like our system so far, click on Register and unlock the power required by serious job searchers.
Any time you conduct a search, the system shows you job matches, ranked by their Relevance Score (RS).
The score is calculated by a proprietary algorithm that uses Intelligent Machine Learning.
The Relevance Score tells you how well the job opportunity matches your search term or terms.
When not logged in, the system is limited to one search term. Scores for single term matches are usually lower.
When you register, log in, and set up multiple terms prioritized by importance, the jobs found for you will receive a much higher Relevance Score.