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Description

Are you passionate about innovating and delivering shopping experiences that empower every Amazon customer based on their unique shopping intent? Do you thrive on tackling complex challenges with Gen AI and advanced ML technologies that uncover deep customer insights, fuel smarter recommendations, and create intuitive shopping journeys?

Join our team and lead innovation in personalized shopping at Amazon scale. You’ll shape how millions of customers interact with a multi-billion product selection, leveraging state-of-the-art machine learning to anticipate their needs, surface the right products, and build trust-driven experiences. If you're excited about pushing the boundaries of AI, solving real-world problems, and making a tangible impact—this is the opportunity for you!

We are looking for an Applied Scientist to join our New York City team. As an Applied Scientist within Personalization, you will apply a range of methodologies to delight customers with personalized shopping experiences. You’ll develop approaches using Gen AI to create content that explains key terminology and product types in a category, recommends the right products, and asks and answers important product questions. You’ll build automated and human in the loop quality auditing techniques, build labelled datasets, and fine-tune models. You’ll develop algorithms to determine where in a customer’s shopping journey they are and evaluate impact of newly launched experiences. And you’ll partner closely with engineers to put your research and ideas into production on Amazon.com.

Key job responsibilities

As an Applied Scientist, you will define and drive the science strategy for personalized shopping experiences and our understanding of customers, collaborating across teams to develop next-generation models that enhance shopping experiences for millions of customers. You will leverage Gen AI, collaborative filtering, deep learning, and large-scale ML models to deliver intelligent, trust-driven recommendations at Amazon scale while reducing dependency on human curation through AI-driven automation with human-in-the-loop optimization.

Your impact will include:

* Setting Science Strategy & Direction – Shape the roadmap for AI-driven personalization, pioneering Gen AI, sequence models, and reinforcement learning to deliver dynamic, context-aware recommendations.

* Driving Cross-Team Collaboration – Partner with applied scientists, engineers, and product teams to ensure ML innovations seamlessly integrate into production at scale.

* Advancing AI-Powered Personalization – Design and refine ranking, retrieval, and recommendation models that adapt to customer behaviors while leveraging Gen AI to generate contextual insights, reducing reliance on manual curation.

* Building Scalable AI Pipelines – Develop automated model training, assessment, and deployment pipelines, integrating human-in-the-loop mechanisms to ensure model quality while minimizing intervention.

* Harnessing Amazon-Scale Data – Analyze vast amounts of historical and real-time data, applying LLMs and statistical techniques to extract actionable insights and improve recommendation effectiveness.

* Deploying & Experimenting at Scale – Validate models through offline benchmarking and large-scale A/B testing, continuously refining personalization strategies.

* Publishing & Thought Leadership – Share white papers in internal Amazon science forums, driving advancements in AI-powered personalization.

A day in the life

As an Applied Scientist, you’ll be at the forefront of defining and driving science strategy across multiple personalization initiatives, working across teams to shape the future of AI-driven shopping experiences. You’ll leverage Generative AI, deep learning models, and predictive algorithms to drive projects from ideation to production.

You’ll collaborate closely with scientists, engineers, and product leaders, aligning research efforts across initiatives and ensuring that breakthroughs in collaborative filtering, ranking, and human-in-the-loop Gen AI drive measurable impact.

Beyond hands-on research and model development, you’ll lead cross-team knowledge sharing, mentor Applied Scientists and Software Engineers, and provide strategic ML consultation across Amazon. Your leadership will help align science efforts across initiatives, ensuring consistency, scalability, and innovation.

Your impact will extend beyond internal teams—you’ll publish papers, file patents, and contribute to Amazon’s ML research community. As a thought leader, you’ll shape not only the models we build but also the broader approach to AI-driven personalization across Amazon’s vast ecosystem.

Every day, you’ll have the opportunity to lead across teams, set science strategy, and deploy AI solutions that redefine how millions of customers shop—delivering smarter, trust-driven, and highly personalized experiences at an unprecedented scale.

About the team

The Discover Innovation team is dedicated to assisting customers in fulfilling their shopping intents and empowering other Amazon teams by leveraging our deep understanding of customer intent. Our team’s mission is to delight customers by helping them discover the right content at the right time, tailoring the shopping experience to the customer’s intent. We are a team of dedicated Scientists, Engineers, and Designers working together to deliver new, personalized shopping experiences. In tailoring the shopping experience to each customer’s intent, we envision operating seamlessly across Amazon as a talented personal shopping assistant — a partner that is trusted, knowledgeable, and understands your unique preferences.

Our vision for Intent-Aware Amazon is for the quality of personalization provided to be a core reason customers choose Amazon, on par with Earth’s largest selection, low prices, and fast and free shipping. In tailoring the shopping experience to each customer’s intent, we envision operating seamlessly across Amazon as a talented personal shopping assistant — a partner that is trusted, knowledgeable, and understands your unique preferences.

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

- 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 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.