Applied Scientist Amazon
Seattle, WA

Job Description

How can we improve the customer experience by tailoring what we display on our pages based on available data? How do we build various data models that helps us innovate different ways to enhance customer experience? What is the relationship to what the customer actually does on the site vs., what they actually buy? How do we do all of this without asking the customer a single question? Sounds fun?

Our team's stated missions is to "grow each customer's relationship with Amazon by leveraging our deep understanding of them to provide relevant and timely product, program, and content recommendations". Recommendations at Amazon is a way to help customers discover products. Our team strives to better understand how customers shop on Amazon (and elsewhere) and build recommendations models to streamline customers' shopping experience by showing the right products at the right time. Understanding the complexities of customers' shopping needs and helping them explore the depth and breadth of Amazon's catalog is a challenge we take on every day.

Using Amazon's large-scale computing resources you will ask research questions about customer behavior, build models to generate recommendations, and run these models directly on the retail website. You will participate in the Amazon ML community and mentor Applied Scientists and software development engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and the retail business and you will measure the impact using scientific tools. We are looking for passionate, hard-working, and talented Applied scientist who have experience building mission critical, high volume applications that customers love. You will have an enormous opportunity to make a large impact on the design, architecture, and implementation of cutting edge products used every day, by people you know.

Internal job description

Some fun facts about our team:

* · Our oncall\ops load is light
* · We have a team-funded drink fridge and snack closet that is kept constantly in stock
* · We regularly go eat lunch together
* · We occasionally have two great pup-azonians (dogs) in the area, and you are welcome to bring more!
* Let us treat you to lunch to learn more!

Loop competencies

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Basic qualifications

MS in Computer Science, Machine Learning, Operational Research, Statistics or a related quantitative field · 2+ years of hands-on experience in predictive modeling and analysis · Strong algorithm development experience · Skills with Java, C++, or other OOP language, as well as with Python, R, MATLAB or similar scripting language · Strong communication and data presentation skills

Preferred qualifications

The ideal candidate will have a PhD in Mathematics, Statistics, Machine Learning, or a related quantitative field, and 7+ years of relevant work experience, including: · Significant peer reviewed scientific contributions in relevant field. · Extensive experience applying theoretical models in an applied environment. · Expertise on a broad set of ML approaches and techniques, ranging from Artificial Neural Networks to Bayesian Non-Parametrics methods. · Strong Experience in Structured Prediction and Dimensionality Reduction. · Expert in more than one more major programming languages (C++, Java, or similar) and at least one scripting language (Perl, Python, or similar). · Proven track record of production achievements in language, search and personalization. · Strong fundamentals in problem solving, algorithm design and complexity analysis. · Strong personal interest in learning, researching, and creating new technologies with high commercial impact. · Experience with defining organizational research and development practices in an industry setting. · Proven track in leading, mentoring and growing teams of scientists (teams of five or more scientists).