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Edinburgh, City of Edinburgh
Machine Learning Scientist - Dynamic Advertising
Overview
Job Description
The Dynamic Advertising team has an opening for an outstanding ML scientist who is passionate about applying advanced ML and statistical techniques to solve real-world challenges. Amazon's Dynamic Advertising program serves millions of personalized Ads every day. We buy Ad impressions in real-time auctions and algorithmically deliver the most relevant Ad. We delight in data, and are constantly working to enhance and improve our models. We relentlessly optimize to keep delivering the best possible Ads for our customers. You will work in an agile and fast-paced team of scientists and software engineers at our development centre in Edinburgh, Scotland. The team is building a number of new advertising products, including dynamic video advertising, to improve the range of offerings for our advertisers and provide new Ad experiences for our customers. As a scientist on the team, you can be involved in every aspect of the process - from idea generation, business analysis and scientific research, through to development and deployment of advanced models - giving you a real sense of ownership. The systems that you help to build will operate at massive scale to display ads to customers around the world. From day one, you will be working with experienced scientists, engineers, and designers who love what they do. We are looking for ML scientists who can delight our customers by continually learning and inventing. Our ideal candidate is an experienced ML scientist who has a track-record of performing analysis and applying statistical techniques to solve real business problems, who has great leadership and communication skills, and who is motivated to achieve results in a fast-paced environment. The position offers an exceptional opportunity to grow your technical and non-technical skills and make a real difference to the Amazon Advertising business. Key responsibilities: Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgment. Collaborate with software engineering teams to integrate successful experimental results into large-scale, highly complex Amazon production systems. Report results in a manner which is both statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment. Promote the culture of experimentation and applied science at Amazon.