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Description

Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. 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.

SB Auction team's role is to develop optimized and fair auction systems for sponsored brands that deliver value for advertisers while enhancing the shopping experience for customers. We collaborate with different teams across the Amazon Ads to build scalable online and offline ML infrastructure systems to accelerate science innovations, facilitate business growth and promote technology innovation.

Key job responsibilities

Key job responsibilities

As a Senior Applied Scientist on this team, you typically play a key role in optimizing ad delivery, improving targeting accuracy, and maximizing revenue generation for advertisers, all while maintaining a seamless user experience, you will:

Develop optimization techniques (e.g., multi-objective optimization) to balance multiple goals, such as maximizing revenue for advertisers, increasing user engagement, and maintaining fair ad distribution.

Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models.

Run A/B experiments, fine-tune the models for real-world effectiveness, ensuring that the ad auction system works optimally in production environments.

Run large-scale experiments to test different auction strategies, bidding algorithms, and ad targeting techniques, using methodologies like multi-arm bandit or reinforcement learning.

Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving

Communicate results and insights clearly to non-technical stakeholders, including product managers, advertisers, and executives, helping them understand the impact of data-driven decisions.

Research new and innovative machine learning approaches.

Recruit Applied Scientists to the team and provide mentorship.

Basic Qualifications

- 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

- Experience with neural deep learning methods and machine learning

Preferred Qualifications

- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.

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 $150,400/year in our lowest geographic market up to $260,000/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.