Sr. Manager Applied Science, MLA

Do you want to join an innovative team of scientists who develop Agentic AI LLM and deep learning based solutions to help Amazon provide the best seller experience across the entire Seller life cycle including recruitment growth support risk mitigation and provide the best customer and seller experience Do you want to build advanced algorithmic systems that help manage the trust and safety of millions of customer interactions every day Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems Are you excited by the opportunity to leverage GenAI and innovate on top of the state-of-the-art large language models to improve customer and seller experience Do you like to build end-to-end business solutions and directly impact the profitability of the company Do you like to innovate and create solutions that have cross-organizational impacts If yes then you may be a great fit to join the Machine Learning Accelerator team.

Key job responsibilities
The scope of a Senior Applied Science Manager in the Selling Partner Services (SPS) Machine Learning Accelerator (MLA) team is lead a team of scientists to research and prototype Machine Learning applications that solve strategic business problems across SPS domains. Additionally the manager collaborates with engineers and business partners to design and implement solutions at scale that are of broad benefit to SPS organizations. They develop large-scale solutions for high impact projects introduce tools and other techniques that can be used to solve problems from various perspectives and show depth and competence in more than one area. They influence the teams technical strategy by making insightful contributions to the teams priorities approach and planning. They develop and introduce tools and practices that streamline the work of the team and they mentor junior team members and participate in hiring.

An MS in CS Machine Learning Statistics Operations Research or in a highly-quantitative field
8 years work experience in relevant science domains including 4 years of managing science teams
4 years of hands-on experience in machine learning deep learning and large data analysis
Superior ML breadth and strong depth
Proficiency with Spark/Python/Perl or other statistical/mathematical packages
Experience with neural deep learning methods and machine learning

A PhD in CS Machine Learning Statistics Operations Research or in a highly-quantitative field
8 years work experience in relevant science domains including 6 years of managing science and engineering teams
6 years of hands-on experience in predictive modeling and large data analysis
Excellent verbal and written communication and data presentation skills
Expertise in large language models or demonstrated ability to develop this expertise quickly
Strong problem solving ability

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status disability or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process including support for the interview or onboarding process please visit for more information. If the country/region youre applying in isnt listed please contact your Recruiting Partner.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $196900/year in our lowest geographic market up to $340300/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge skills and experience. Amazon is a total compensation company. Dependent on the position offered equity sign-on payments and other forms of compensation may be provided as part of a total compensation package in addition to a full range of medical financial and/or other benefits. For more information please visit This position will remain posted until filled. Applicants should apply via our internal or external career site.