The Brand Intelligence and Agentic AI program represents the voice of customers with respect to brands: brand recognition, reputation, value, quality, and overall appeal. The team as part of Amazon Retail tech org is hiring an experienced Sr. Applied Scientist to guide the team on science strategies, design ML solutions (including the new Agentic AI agents) to solve customer-facing brand shopping challenges at scale, and grow and influence the broader science community. The team uses Machine Learning, Deep Learning, LLM and Agentic AI to derive actionable insights from understanding customer shopping intent and preferences on brands (well recognized brands, premium brands, new and trending brands, relevant top brands matching customer searches, or individual customer-level brand preference), and develop and experiment with ML solutions to deliver business impact. We also start working with marketing team to build AI agents to assist them better in their marketing journey with brands insights and knowledge.
We are a science-focused team incubating and building disruptive solutions to solve large-scale shopping recommendation and personalization problems to assist our customers easily discover relevant and valuable brands and selection, as well as to help brand owners being successful to reach promising customers.
This is a unique, high visibility opportunity for someone who wants to have direct impact solving customer-facing problems, dive deep into large-scale science problems esp. in the new Agentic AI world, enable measurable actions on the Consumer economy, and work closely with scientists.
Key job responsibilities
Work with Product and Engineering to shape the product vision and provide forward-looking science strategies.
Lead scientists design and build machine-learning and LLM solutions.
Collaborate with partner teams on customer-facing search and browse experiences that will utilize the data and ML models to better serve customers' brand shopping experience.
Perform hands-on data analysis, build machine-learning models, run regular A/B tests, and communicate the impact to senior management.
Drive continued scientific innovations (Agentic AI e.g.) as a thought leader and practitioner, and understand marketing needs on brands understanding.
Provide technical and career development guidance to both scientists and engineers in the organization.
