About the job
The Brand Protection Science team designs and builds highly performing and scalable AI models using machine learning, deep learning, LLM/Gen AI to identify and prevent infringement abuse and counterfeit on behalf of buyer, seller and brand owners worldwide. As an Applied Science Manager, you will be lead a group of highly talented scientists, and partner with Tech, Business and Operations, to build the strategic vision for brand protection, drive execution and launch scalable AI solutions operating on billions of Amazon product listings WW for Brand Protection.
Responsibilities
You will lead a team of Applied Scientist to work backwards from customer needs and solve complex scientific problems that have a high business and customer impact. As a manager, You will be the thought leader for inventing novel science solutions using SOTA ML techniques including LLM and GenAI. You partner with your stakeholders and leadership to define the science vision and strategies for your team. You have excellent communication skills to explain complex scientific approaches to a variety of stakeholders and customers, and bridge the gap between science, tech, and business,. You are accountable for the science vision and strategic direction of your team, the artifacts they provide, and any technologies owned. You will establish structures that enable your team to consistently deliver. You will also lead your team to leverage the broader Amazon scientific community, and build a team culture that focuses on bringing research to production, removing road blockers, and delivering more results for Amazon customers. You are strategic about the team members' growth and provide those interested with opportunities to demonstrate higher level role scope, impact, complexity and leadership.
Qualifications
Minimum
PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field, or Master's degree and 4+ years of building machine learning models or developing algorithms for business application experience
2+ years of scientists or machine learning engineers management experience
Knowledge of machine learning approaches and algorithms
Knowledge of ML, NLP, Information Retrieval and Analytics
5+ years of building machine learning models or developing algorithms for business application experience
Preferred
Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers
Experience with deep learning libraries such as PyTorch, TensorFlow, MxNet Research publications in computer vision, deep learning or machine learning at peer-reviewed workshops, conferences or journals
Experience with the Scrum methodology (or similar alternatives) for agile software development