Amazon

Principal Applied Scientist

3+ months agoNew York, NY

DESCRIPTION

The Advertising Supply & Monetization team determines the display ads shown to Amazon shoppers, and is responsible for delivering an engaging, relevant and personalized ad experience. We optimize entire pages of retail and advertising content in near real-time for millions of shoppers each day, maximizing the long-term value that display advertising creates for Amazon.

As a Principal Scientist, your core efforts will involve leading the evolution of real-time pricing and valuation models for advertising in the context of delivering a highly personalized content experience on retail shopping pages. You will invent measures to quantify the impact on shopping from changes in ad quality, relevance, and the general user experience with ads. You will deploy services to help us better optimize against these measures, and identify opportunities to improve bidding strategies into the Amazon auction to deliver impact to Amazon's shopper and advertiser customers.

This is a unique, high visibility opportunity for a talented, motivated individual to deliver direct impact to Amazon shoppers, have business impact, and dive deep into high-scale low latency problems at the cutting edge of machine learning, optimization and economics. As a Principal Scientist on the Advertising Supply & Monetization team, you will work closely with scientists, economists, engineers and business leaders to translate business and functional requirements into concrete deliverables, including design, development and testing. You will act as a thought leader and forward thinker on the team anticipating obstacles to success and helping us avoid common failure modes. You will hire and coach junior scientists and partner with engineering leaders to build efficient scalable systems.

This role offers a compelling mix of contemporary research problems that are unique to Amazon based on the interplay between retail and advertising, scale and richness of data sets, emphasis on user experience and relevance, and complexity introduced by concurrent experimentation across teams. In addition, you will deliver significant customer and business impact by shaping the future of the Amazon shopping experience and delivering growth to the advertising business.

BASIC QUALIFICATIONS

• PhD in Computer Science, Mathematics, Statistics, or a related quantitative field
• 10+ years industry experience building successful production software systems
• 7+ years of applied research experience
• Experience with one of the following areas: machine learning technologies, Reinforcement Learning, Deep Learning, Natural Language Processing (NLP), information retrieval and related applications
• Proven ability to implement, operate, and deliver results via innovation at large scale
• Experience in modern programming languages (Python, Java, C++, C)
• Experience communicating with executives and non-technical leaders
• Strong Computer Science fundamentals in data structures, algorithm design, statistics and system design

PREFERRED QUALIFICATIONS

• Significant peer reviewed scientific contributions in Reinforcement Learning, Deep Learning, Natural Language Processing, and related 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
• Expert in more than one more major programming languages (C++, Java, or similar) and at least one scripting language (Python or similar)
• 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 scientist)
• 10+ years of industry experience in applying Machine Learning/Deep Learning/Reinforcement Learning to optimization and recommendation problems

Job ID: Amazon-1409028