Applied Science Manager, SB Auction Team

Amazon Advertising is at the forefront of shaping the future of advertising technology, and our Auction team in Sponsored Brands is pivotal in driving this innovation. We are seeking a passionate and visionary Applied Science Manager to lead a team of Applied Scientists, Data Scientists, and Machine Learning Engineers dedicated to advancing auction mechanisms within our advertising ecosystem. This role is essential for developing optimized and fair auction systems that deliver value for advertisers while enhancing the shopping experience for customers.

As the leader of the Auction Science team, you will drive the scientific and engineering strategy to innovate and build scalable solutions that improve auction design and efficiency. Your work will directly contribute to aligning advertiser needs with customer interests, ensuring that our auction systems maximize relevance and value for all stakeholders. You will be responsible for developing and managing a research agenda that balances short-term business impact with long-term strategic investments, driving continued scientific innovation in the fields of economics, game theory, operations research, and machine learning.

In this role, you will collaborate closely with cross-functional teams to execute complex projects, ensuring alignment across science, engineering, and product strategies with business objectives. You will be hands-on in data analysis, machine learning model development, and A/B testing, using your insights to inform decisions and communicate impact to senior management. As a leader, you will foster a culture of innovation, providing technical and scientific mentorship to your team while guiding them in delivering solutions that elevate both advertiser satisfaction and the overall shopping experience on Amazon. Additionally, you will play a key role in hiring and developing top talent, ensuring the growth and success of your team.

BASIC QUALIFICATIONS

- 4+ years of applied research experience

- 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 6+ years of applied research experience

- Experience programming in Java, C++, Python or related language

- Strong knowledge of auction theory, economics, game theory, or operations research, with experience in applying these principles in practical scenarios.

PREFERRED QUALIFICATIONS

- Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers

- Experience building machine learning models or developing algorithms for business application

- Strong publication record in relevant scientific fields.

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

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.

---