Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. 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 organization'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!
Our team, Off-Search Sourcing and Relevance, has a mission to deliver relevant and useful shopping experience at non-Search pages at Amazon.com. We innovate technology solutions, develop state-of-the-art machine learning models that incorporate deep product and shopper understanding, and conduct A/B tests to ensure that we identify all useful and relevant advertisements and provide them to downstream systems for click through prediction and ad auction.
We are looking for a passionate Applied Scientist who has technical expertise in information retrieval, Natural Language Processing (NLP), and Large Language Models (LLM). In addition to having hands-on experience in building ML-based solutions, an ideal candidate should be able to create and articulate a customer-centric science vision, show willingness to continuously learn about new scientific approaches, and enjoy operating in startup-like environment.
Key job responsibilities
- 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 simultaneously increase traffic monetization, merchandise sales and advertiser and shopper experience
- 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.
