DESCRIPTION

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 deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day!

The Response Prediction team builds machine-learning models and infrastructure to support the Sponsored Products Ads business. Through precise estimation of shoppers' response to ads (e.g. clicks or product purchases), this team helps deliver the most relevant ads experience to shoppers, improves advertisers' ROI, and optimizes Amazon's long-term monetization. The team builds and operates one of the largest ML workflows in WW Advertising, serving Search and Detail Pages. Additionally, it also owns the horizontal ML infrastructure to support various ML use cases - from offline ML pipelines to online model inferencing and model management services.

Team video https://youtu.be/zD_6Lzw8raE


Key job responsibilities

As a Sr. Applied Scientist on this team, you will:
- Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity.
- Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience.
- Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with service engineers to bring your ML models in production
- 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.
- Research new and innovative machine learning approaches
- Publish papers in internal and external conferences