Applied Scientist, SB Response Prediction and Auction Science Team

Description

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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 Response Prediction and Auction Science team in Sponsored Brands is pivotal in driving this innovation.

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SB Response Prediction and Auction Science Team predicts how shoppers interact with Sponsored Brands ads and designs auction systems to drive values for advertisers, shoppers and Amazon ads. 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.

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Key job responsibilities

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As an 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:

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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.

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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.

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

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

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

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

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Research new and innovative machine learning approaches.

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Basic Qualifications

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  3+ years of building models for business application experience

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  PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience

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

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  Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

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Preferred Qualifications

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  1. Knowledge of optimization algorithms for multi-objective problems (e.g., gradient descent, linear programming).
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  1. Strong background in probability theory, game theory, and auction theory (important for designing competitive auction systems).
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  1. Proficiency in reinforcement learning, particularly for decision-making problems like bidding strategies and auction design.
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Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

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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 you're applying in isn't listed, please contact your Recruiting Partner.

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Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $223,400/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.