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Amazon Senior Applied Scientist, Worldwide Defect Elimination in Seattle, Washington

Description

As a Senior Applied Scientist in Worldwide Defect Elimination at Amazon, your role is pivotal in leveraging advanced AI and ML techniques to address customer issues at scale. You'll lead the development of innovative solutions that summarize and detect problems, organize them using ontologies, and pinpoint root causes within Amazon systems. Your expertise will drive the identification of responsible stakeholders and enable swift resolution. You will utilize the latest in AI/ML to to develop an LLM ecosystem that can comb over our billions of contacts (using a combination of media). As a part of this role, you will collaborate with a large team of experts in the field and move the state of research forward.

Key job responsibilities

  • Research and Development of Cutting Edge Modeling techniques for NLP, NLU, and Prediction.

  • Establishing scientific culture and standards for all the scientists (publication, best practices, reviews, etc.)

  • Delivering solutions to the core business problems above and influencing business partners

  • Develop scientific talent on the team

A day in the life

If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!

Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include:

  1. Medical, Dental, and Vision Coverage

  2. Maternity and Parental Leave Options

  3. Paid Time Off (PTO)

  4. 401(k) Plan

About the team

The Worldwide defect elimination team's mission is to understand and resolve all issues impacting customers at scale. The Economics team is a force multiplier within this group, helping to understand the impact of these issues, changes to our policies, and optimizing the customer experience.

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

Seattle, WA, USA

Basic Qualifications

  • 5+ 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

Preferred Qualifications

  • Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy 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. This position will remain posted until filled. Applicants should apply via our internal or external career site.

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