Sr Applied Scientist, Sponsored Products and Brands
Job ID: 3106321 | Amazon.com Services LLC
The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through cutting-edge generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising.
Responsibilities

Collaborate with business, engineering and science leaders to establish science optimization and monetization roadmap for Amazon Retail Ad Service
Drive alignment across organizations for science, engineering and product strategy to achieve business goals
Lead/guide scientists and engineers across teams to develop, test, launch and improve of science models designed to optimize the shopper experience and deliver long term value for Amazon advertisers and third party retailers
Develop state of the art experimental approaches and ML models to keep up with our growing needs and diverse set of customers.
Participate in the Science hiring process as well as mentor other scientists - improving their skills, their knowledge of your solutions, and their ability to get things done.

Basic Qualifications

PhD, or Master's degree and 6+ years of applied research experience
3+ years of building machine learning models for business application experience
Experience programming in Java, C++, Python or related language
Experience with neural deep learning methods and machine learning
Master's degree, or a PhD and experience with generative deep learning models applicable to the creation of synthetic humans like CNNs, GANs, VAEs and NF

Preferred Qualifications

Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
Experience with large scale distributed systems such as Hadoop, Spark etc.
Experience in auctions or mechanism design
Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability

Equal Opportunity and Accommodations
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
Compensation and Benefits
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits . This position will remain posted until filled. Applicants should apply via our internal or external career site.
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