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Description

Passionate about books? The Amazon Books personalization team is looking for a talented Applied Scientist III to help invent, design, and deliver innovative science solutions to make it easier for millions of customers to find the next book they will love. In this role you will:

- Be a part of a dynamic team of scientists, economists, engineers, analysts, and business partners.

- Use Amazon's large-scale computing and data resources to generate deep understandings of our customers, and products.

- Build models to generate recommendations and run them as large scale A/B tests directly on the retail website.

- Design, implement, and deliver novel solutions to some of Amazon's most complex problems.

Key job responsibilities

The role is focused on recommender systems bridging Generative AI, Natural Language Processing (NLP), Reinforcement Learning (RL), graph networks, and deep learning to help find the next great read for Books customers. You will build recommendation model pipelines, identify technical opportunities within complex deep learning-based recommendation models, and work with engineering and product leaders to power customer-facing recommendations.

As part of the team, you will be exposed to all of these areas and have opportunities to hone and apply your skills across our problem space.

A day in the life

From day-to-day, you will research and develop models that power customer facing recommendations, design and implement A/B test experiments, as well as collaborate with engineers, product, and other scientists to get machine learning solutions into production.

About the team

We are Books Core Recommendations, a collaborative group of 5-7 scientists, 2 product leaders, and 2 engineering teams that aims to help find the right next read for customers through high quality personalized book recommendation experiences. Books Core Recommendations is a part of the Books Content Demand organization, which focuses on surfacing the best books for customers wherever they are in their current book journey.

Basic Qualifications

- 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 modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.

Preferred Qualifications

- Experience with popular deep learning frameworks such as MxNet and Tensor Flow.

- Experience with large scale distributed systems such as Hadoop, Spark etc.

- Experience in applied research

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.