Sr. Applied Scientist, Sponsored Products Scaled Controls

Sr. Applied Scientist

Amazon's Sponsored Products advertising business is one of the fastest growing areas in the company. The Sponsored Products Marketplace team creates and optimizes the systems that match advertiser demand with page supply using a combination of data-driven product innovation, machine learning, big data analytics, and low latency/high-volume engineering. By the time organic search results are ready, we've processed all of the candidate ads and decided which ones are delivered to the page. We do that billions of times per day, resulting in millions of engagements with products that otherwise might not have been seen by shoppers. The business and technical challenges are significant. Fortunately, we have a broad mandate to experiment and innovate, and a seemingly endless range of new opportunities to build a big, sustainable business that helps Amazon continuously delight all of our customers. We're looking for an innovative and customer-obsessed Sr. Applied Scientist who can help us take our products to the next level of quality and performance by creating state-of-the-art models to improve our ability to optimize performance, forecast the impact of advertiser actions, and enable advertisers to scale through impactful features. We embrace leaders with a startup mentality -- those who have a disruptive yet clear mission and purpose, an unambiguous owner's mindset, and a relentless obsession for delivering amazing products. As Sr. Applied Scientist on the Scalable Controls team, you will work alongside business leaders, other scientists, and software engineers to deliver rules that algorithmically manage ads using ML, DL, and R techniques. You will be responsible for bridging the experimental domain with the production domain by building robust and efficient computational pipelines to scale up models, keeping the models fresh, and ensuring that real-world corner cases are handled correctly. You'll own significant products and features from inception through launch, and will work with Product Managers, other Scientists, and Engineers to make your efforts wildly successful. You will lead the science program for our team, providing input to strategic decision making on topics such as program direction/vision, roadmap, and staffing. Characteristics indicative of success in this role:

• Highly analytical: You solve problems in ways that can be backed up with verifiable data. You focus on driving processes, tools, and statistical methods which support rational decision-making.
• Technically strong: You aren't satisfied by performing 'as expected' and push the limits past conventional boundaries. Your dial goes to '11'.
• Engaged by ambiguity: You're able to explore new problem spaces with unique constraints and non-obvious solutions.
• Team obsessed individual contributor: You help grow your team members to achieve great results. You've learned that big plans generally involve collaboration and great communications.
• Quality obsessed: You recognize that professional scientists build high quality model development and evaluation frameworks to ensure that their models can provably meet launch criteria, or efficiently iterate in the framework until they do.
• Humbitious: You're ambitious, yet humble. You recognize that there's always opportunity for improvement. You use introspection and feedback from teammates and peers to raise the bar.

Key job responsibilities

• Apply machine learning and analytical techniques to create scalable solutions for business problems
• Work closely with software engineering and product teams across the organization to drive model implementations and new feature creations
• Work closely with business stakeholders to identify opportunities for current model improvements and new models to significantly benefit the business bottom-line
• Collaborate with scientists within the Ads organization as well as other parts of Amazon to share learnings move the state-of-the-art forward
• Establish scalable, efficient, automated processes for data analyses, model development, model validation and model implementation
• Research and implement novel machine learning and statistical approaches