Senior Applied Scientist , Machine Learning
- Seattle, WA
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses. As a core product offering within our advertising portfolio, Sponsored Products (SP) helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The SP team's primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth.
We are looking for senior Applied Scientists who have a deep passion for building machine-learning solutions, ability to communicate data insights and scientific vision, and execute strategic projects.
In this role you will:
• Build machine learning models and utilize data analysis to deliver scalable solutions to business problems.
• Run A/B experiments, gather data, and perform statistical analysis.
• Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving.
• Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production
• Research new machine learning approaches.
Impact and Career Growth:
• You will invent new shopper and advertiser experiences, and accelerate the pace of Machine Learning and Optimization.
• Influence customer facing shopping experiences to helping suppliers grow their retail business and the auction dynamics that leverage native advertising, this role will be powering the engine of one the fastest growing businesses at Amazon.
• Define a long-term science vision for our ad marketplace, driven fundamentally from the needs of our customers, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams.
• This is a role that combines science leadership, organizational ability, technical strength, product focus and business understanding.
• In the immediate term, this role requires (a) addressing principles of allocation function and pricing in ad marketplace auctions, (b) developing efficient algorithms for multi-objective optimization and AI control methods to find operating points for the ad marketplace auctions and to evolve them, and (c) develop science talent around machine learning, Economics and optimization for WW Advertising.
Why you love this opportunity:
• Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales.
• Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products.
• We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities.
Team video ~ https://youtu.be/zD_6Lzw8raE
• M.S. or Ph.D. in Computer Science, Information Retrieval, Machine Learning, Statistics, Applied Mathematics, Natural Language Processing, or related discipline.
• Breadth and depth knowledge of machine learning algorithms and best practices.
• At least 4 years of hands-on experience in building Machine Learning solutions to solve real-world problems.
• At least 4 years of experience with computer science fundamentals in object-oriented design, data structures, algorithm design, problem solving, and complexity analysis.
• At least 4 years of experience with, at least, one model programming language such as Java, Python, Scala, C++.
• Ph.D. in quantitative field with a strong Machine Learning background.
• Experience in building large-scale machine-learning models for online recommendation, ads ranking, personalization, or search, etc.
• Experience with Big Data technologies such as AWS, Hadoop, Spark, Pig, Hive, Lucene/SOLR or Storm/Samza.
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