About the job
The Amazon Publisher Monetization Stores team is seeking an experienced Manager, Applied Science to lead our Stores Supply Science team and our Stores Supply Applied Science Engineering team. In this role, you will be responsible for developing novel machine learning and optimization solutions to drive improvements in the monetization of digital content for Amazon's publishing partners. You will collaborate closely with product, engineering, and business stakeholders to identify high-impact opportunities, define technical roadmaps, and deliver innovative solutions at scale. Equally importantly you will represent APM Stores Science across the broader Ads science community (e.g. Sponsored Products, Sponsored Brands, DSP and Ads Econ) and drive collaboration and harmonization. This is an exciting opportunity to leverage your depth of applied science expertise to shape the future of Amazon's publisher monetization platform and have a significant impact on the business.
Responsibilities
Lead the Stores Supply Science team and Applied Science Engineering teams as a direct manager, setting the technical vision and implementation, managing performance, and developing your team members
Work closely with product, engineering, and business stakeholders to define the technical roadmap and ensure the delivery of high-impact solutions
Represent APM Stores Science across the broader Ads science community
Identify new opportunities to leverage data and advanced analytics to unlock value for Amazon's publishing partners
Foster a culture of innovation, agility, and customer obsession within your teams
Qualifications
Minimum
3+ years of scientists or machine learning engineers management experience
3+ years of building machine learning models for business application experience
PhD, or Master's degree and 5+ years of applied research experience
Knowledge of ML, NLP, Information Retrieval and Analytics
Experience programming in Java, C++, Python or related language
4+ years of applied research experience
Preferred
Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers