Amazon Advertising operates at the intersection of eCommerce and advertising, helping shoppers find and discover anything they want to buy. We help advertisers reach Amazon customers on Amazon.com. Our team drives innovation for the retail shopping site publisher. We are building the ML algorithms that optimize auction levers in the core auction, affecting which ads millions of daily Amazon shoppers see and how those ads are priced. This is a great time to join; we are still in early stages deploying the first generation of algorithms to drive yield and delight shoppers.
As we continue building to out our team, we are looking for a Senior Applied Scientist to lead research, design, experiment and implementation of cutting edge algorithms for complex Stores Display publisher use cases. You will collaborate with Amazon demand programs and front-end teams to navigate how the entire advertising stack works end-to-end. You will work collaboratively with software engineers, data engineers, and other scientists on the team to deploy production algorithms that support the billions of ad auctions run daily and set a high bar for science and engineering excellence. This is a rare opportunity to be a foundational member with huge potential for impact and innovating new user experiences at Amazon.
Despite being part of a large business, we embrace a start-up mentality. This is an opportunity for massive impact and a lot of autonomy.
* Candidates can be based in NYC or Arlington (HQ2) *
Key job responsibilities
As a Senior Applied Scientist on this team, you will:
* Be the technical leader in Machine Learning; lead efforts within this team and across other teams.
* Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and retail merchandise sales, without compromising the shopper experience.
* Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity.
* Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models.
* 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.
* Recruit Applied Scientists to the team and provide mentorship.
