Amazon Advertising is one of Amazon's fastest growing and most profitable businesses. Amazon's advertising portfolio helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The 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. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! The ADSP Forecasting team's vision is to build the best in class forecasting products offered by any DSP to allow advertisers to forecast campaign outcomes across the full market funnel. Our goal is to empower advertisers using Amazon demand side platform to make informed decisions by providing predictions and recommendations of supply and ad-performance. Our forecasting models and analytical solutions will also help internal teams (sales, PSC, supply desk etc) to gain insights into forecasted supply, demand and ad performance to make the best business decisions. The team comprises scientists and engineers who own end-to-end projects - data collection, analysis, ideation, and prototyping, to development, metrics and monitoring. The models and services are integrated directly with Amazon's Ads eco system and the forecasts are used to drive key business decisions at the VP/SVP level. We are a team of Applied Scientists and Engineers, who are passionate about solving technical problems in the Ad Forecasting space with models using Machine Learning, Bayesian Statistics, etc. You will join a group of highly talented PhDs with diverse background to design, prototype, and implement models to deliver impact directly to customers. You will have the opportunity to present your work in science communities and to leadership

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 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.