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Senior Applied Scientist, US Prime and Marketing Tech
Senior Applied Scientist, US Prime and Marketing Tech-October 2024
Seattle
Oct 19, 2026
ABOUT AMAZON
Our mission is to be the world’s most customer-centric company.
10,000+ employees
Technology
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About Senior Applied Scientist, US Prime and Marketing Tech

  Description

  Are you excited to help customers discover the hottest and best reviewed products?

  The Discovery Tech team helps customers discover and engage with new, popular and relevant products across Amazon worldwide. We do this by combining technology, science, and innovation to build new customer-facing features and experiences alongside cutting edge tools for marketers. You will be responsible for creating and building critical services that automatically generate, target, and optimize Amazon’s cross-category marketing and merchandising. Through the enablement of intelligent marketing campaigns that leverage machine-learning models, you will help to deliver the best possible shopping experience for Amazon’s customers all over the globe.

  We are looking for analytical problem solvers who enjoy diving into data, excited about data science and statistics, can multi-task, and can credibly interface between engineering teams and business stakeholders. Your analytical abilities, business understanding, and technical savvy will be used to identify specific and actionable opportunities to solve existing business problems and look around corners for future opportunities. Your domain spans the design, development, testing, and deployment of data-driven and highly scalable machine learning solutions in product recommendation.

  As an Applied Scientist, you bring business and industry context to science and technology decisions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions.

  To know more about Amazon science, please visit https://www.amazon.science

  We are open to hiring candidates to work out of one of the following locations:

  Seattle, WA, USA

  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

  Knowledge of programming languages such as C/C++, Python, Java or Perl

  Experience programming in Java, C++, Python or related language

  Experience with neural deep learning methods and machine learning

  Preferred Qualifications

  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.

  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 $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. Applicants should apply via our internal or external career site.

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