Job summaryAre you passionate about solving some of the world's largest scale and most complex deep-learning science problems, while transforming consumer shopping experience over the next few years? Our Onsite Publishing (OSP) organization is transforming Amazon.com by helping customers discover and research what they want to purchase by connecting them with product review content developed by creators of all types – from large publishers to individual influencers. This advises customers to make the best buying decisions by bringing third party, expert content into Amazon. Onsite publishing sources high-quality content from third-party publishers and influencers at scale, moderates, annotates, and vends it to internal experience owners within Amazon (e.g. Video Shopping, Storefronts, #FoundItonAmazon, StyleFeed), and monitors content performance. OSP also owns the content publishing tools along with select creator experiences. We believe that providing relevant, quality content across Amazon will increase long-term customer engagement along with product discovery and research.The challenge that OSP owns is unique at Amazon in two ways: 1) OSP owns the end-to-end content workflows from ingestion to vending, and we must build robust Science methods to automate this at Amazon scale, and 2) We are building our workflows in a content agnostic way, but we will need ways to solve multi-content optimization, e.g. publisher articles that include photos and/or videos. and content ranking.We are seeking a Principal Applied Scientist to both tackle our most complex problems and raise the bar for our Science colleagues within Amazon's Customer Engagement organization . This candidate will make long term investments in our unique challenge - to lead Amazon in the automated extraction, understanding, processing of knowledge and ranking of content. The knowledge we need to ingest are wide ranging in data type coverage, and are from varying external source formats. To accomplish our multi-year challenges, we need the candidate to lead R&D of ML models to ingest content from disparate sources as opposed to requiring creators to submit content in specific, rigid ways which currently stifles ingestion and on-boarding of content at scale today.Key job responsibilitiesThis candidate will be the intellectual thought leader for the team, setting the working backwards path for our most complex problems. This individual will work closely with our Applied Scientist team, Principal Engineers, software development engineers, product managers and data engineers. In addition to solving problems, this candidate will raise the bar for our team by reviewing proposals and technical designs for the portfolio of problems we solve. The successful candidate will have a proven track record of breaking down and solving complex Science problems. In addition, you have strong business judgement and are an excellent communicator, with proven ability to influence at the most senior levels. You have the passion and experience to closely work with a diverse and innovative team that will delight customers and set new standards in our space. You are entrepreneurial and enjoy working in a dynamic, ambiguous, fast-paced environment. You are humble and have a track record of exceptional performance. People trust and respect you, and they like to work with and for you.A day in the lifeAs the Principal Applied Scientist, you will work closely with the engineering, product, and data leaders to ensure we are prioritizing the right problems, ensuring long-term and resilient designs, and meeting the high quality bar and deadlines. You will take ownership over select problems and work with the team to implement into production. You will also spend time reviewing designs and mentoring our junior Science team members. You will also participate in learning opportunities for the broader Consumer Engagement Science community. About the teamOSP consists of 1) Software Development Engineers, including a Principal Engineer, and Software Managers and Technical Program Managers, 2) Science leaders, including multiple Applied Scientists, Data Scientists, and Data Engineers, and 3) Product Manager-Technical, which help prioritize and work backwards to experiment.