At Amazon, we're building Earth's most desired destination for Creators to monetize their unique skills, connect with customers, and help brands expand their reach. Within our Creators organization, we're seeking an Applied Science Manager to lead a team of scientists developing innovative solutions that help creators grow their businesses through Amazon's services. You'll lead the development of machine learning, statistical, and economics solutions to enhance creator programs and develop features that help creators achieve their business goals.
Key job responsibilities
As an Applied Science Manager you will:
* Lead and grow a team of Applied Scientists, Economists, and Data Scientists working to solve complex problems in creator services and engagement
* Define and execute on the science vision for the team, working backwards from creator needs to translate product ideas into concrete deliverables
* Drive the development of scalable machine learning solutions that support flexible earnings programs and help optimize creator success
* Partner with engineering teams to implement production-ready scientific solutions
* Recruit, mentor, and develop top scientific talent
* Foster a culture of innovation while maintaining high scientific standards
* Review and provide guidance on experimental design, methodology, and implementation
* Collaborate with product, engineering, and business teams to continuously improve creator experiences
A day in the life
* Start your day reviewing key metrics and experimental results with your team
* Participate in design reviews for new models used in products such as creator compensation, content generation, or risk identification
* Meet with product managers to align on roadmap priorities and scientific requirements
* Provide technical mentorship to team members on their projects
* Review and give feedback on scientific documentation and methodology
* Collaborate with engineering partners on implementation approaches
* Lead team meetings focused on scientific innovation and best practices
* Engage with the broader Amazon science community to share learnings and stay current with latest developments
About the team
The Creators team focuses on continuously improving services for commerce creators and their content, working to deliver consistent excellence in creator experiences. Our science team is a highly collaborative, inclusive team located in Seattle and New York City. We value mentorship, growth, and innovation, and we’re building an environment that celebrates knowledge. We play a crucial role across several key areas:
* Creator Success: We develop and implement ML solutions to help creators understand and improve their performance through data-driven insights
* Program Integrity: Our team builds advanced models to maintain program quality and ensure a trustworthy environment for creators and customers
* Innovation in Content Creation: We develop and evaluate tools that help creators produce engaging content more efficiently, incorporating responsible AI technology to enhance creator productivity
* Measurement Sciences: We advance experimental design methodologies to better understand the impact of new features and programs, helping inform data-driven business decisions
* Creator Compensation: Our team works to enhance earning opportunities by developing effective incentive programs
The science organization partners closely with product, engineering, and business teams to translate research into impactful solutions. We maintain high standards for scientific rigor while moving quickly to deliver value in a dynamic business environment.
This role presents an opportunity to work on challenging technical problems at scale, with access to unique datasets and the ability to make a direct impact on creators' success through innovative applications of machine learning, causal inference, and economic modeling.
- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field, or Master's degree and 3+ years of applied research experience
- 2+ years of scientists or machine learning engineers management experience
- 3+ years of building machine learning models or developing algorithms for business application experience- Knowledge of machine learning approaches and algorithms
- Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers
- Experience hiring and growing top talent
- Experience communicating with users, other technical teams, and management to collect requirements, describe software product features, and technical designs
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 $165,500/year in our lowest geographic market up to $286,000/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.
