Are you excited by the idea of developing personalized experiences for Amazon customers as they shop? Are you looking for new challenges and to solve hard science problems while applying state-of-the-art recommendation system modeling techniques? Join us and you'll help millions of customers make informed purchase decisions while also advancing the state of Amazon's science by publishing research!
Key job responsibilities
- Participate in the design, development, evaluation, deployment and updating of data-driven models for shopping personalization.
- Use expertise in supervised and uplift learning algorithms to improve ML performance
- Research and implement novel ML and statistical approaches to add value to the business.
- Design A/B tests and conduct statistical analysis on their results
- Work with distributed machine learning and statistical algorithms to harness enormous volumes of data at scale to serve our customers
- Work closely with internal stakeholders like the business teams, engineering teams and partner teams and align them with respect to your focus area
- Present and publish science research, contributing to Amazon's science community
- Mentor junior engineers and scientists.
About the team
Our team's mission is to surface the right payments-related recommendations to customers at the right time, helping create a rewarding and successful shopping experience for Amazon's customers. Our team's culture is highly collaborative, with an emphasis on supporting each other and learning from one another. We dedicate time each week to focus on personal development and expanding our knowledge as a team. We also highly value having a big impact, both for Amazon's business and for our customers.- 4+ years of applied research experience
- 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 language
- Experience with neural deep learning methods and machine learning
- Experience with reinforcement learning
- Publication record on machine learning methods- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.
- Experience with causal inference modeling.
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 $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.
