Orchestrating the selection of one out of tens of millions of ads, honoring advertiser targeting intent for hundreds of thousands of advertisers while ensuring great shopper experience for billions of shoppers millions of times per second on a latency of tens of milliseconds is not a trivial task. The demand retrieval team within the Amazon DSP organisation deals with this challenge, developing and operating machine learning models that match ads opportunities with the most relevant ads to deliver the right messages to the right customers at the right time.
We are looking for an Applied Scientist to optimize ad matching for Amazon’s programmatic advertisement products. In this role you will lead the design and implementation of solutions for performance sourcing, using behavioural information on customers’ interactions with Amazon and other owned and operated businesses as well as contextual information about the bid request to predict their propensity to convert, in turn driving better advertising campaign outcomes. Your work will affect multi-billion dollar businesses, and you will be responsible for designing, testing and delivering significant breakthrough's for Amazon's business.
Successful candidates will have strong technical ability, excellent teamwork, communication skills, and a motivation to achieve business results in a fast-paced environment.
Key job responsibilities
- Design and implement deep learning models to match the right customers with the right ads across different verticals, geographies, and ads formats.
- Investigate new ML techniques such as multi-task learning to ensure that models can operate for a variety of advertisers in multiple industries and with different volumes of conversion events.
- Improve the performance, generalisation and scalability of models by introducing new features and enhancing models’ architecture.
- Work side by side with our engineers to deliver code changes impacting our ads stack, working with very large datasets and high throughput production systems.
- Rapidly prototype and test many possible hypotheses/implementation alternatives in a high-ambiguity environment, making use of both quantitative analysis and business judgement.
- Be immersed in Amazon's advertisers and their objectives, and think long-term about how to turn those objectives into products and technical capabilities.
- Understand the latest literature on machine learning for recommender and advertising systems, contributing to guiding strategic investment for the organization.
A day in the life You will partner with our product and engineering teams, bringing your own ideas to the conversation and aligning on work, adjusting priorities based on business requirements and fast iteration on experiments. You will have a strong theoretical understanding of modern ML techniques and methodologies, and the software engineering and data processing skills to deploy these using the large-scale datasets we deal with in advertising.
About the team The Demand Retrieval team is responsible for designing, implementing, deploying and operating machine learning models that match bid opportunities to ads demand based on performance, campaign delivery, and targeting objectives specified by advertisers. We measure the success of our approaches based on offline experimentation and and online metrics that measure the impact of our matching models on campaign KPIs (e.g.: cost per action, return on ads investment, budgets delivered, and targeting precision).
Basic Qualifications
- PhD, or a Master's degree and experience in CS, CE, ML or related field research
- Experience programming in Java, C++, Python or related language
- Experience in building machine learning models for business application
- Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
Preferred Qualifications
- Experience in retrieval and ranking systems as applied to advertising or recommender systems
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