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Sr. Manager Applied Science, MLA
Seattle, Washington, USAPosted 8 months ago
Full-timeonsite
Job Description
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 team’s technical strategy by making insightful contributions to the team’s 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.
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,
• 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 https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t 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 $196,900/year in our lowest geographic market up to $340,300/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 https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
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 team’s technical strategy by making insightful contributions to the team’s 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.
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,
• 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 https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t 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 $196,900/year in our lowest geographic market up to $340,300/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 https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.