Amazon's Automated Marketing and Events (AME) team builds and manages automated marketing programs at scale, determining how and where to most efficiently invest.

Our mission is to engage customers both onsite and off Amazon's websites with the right products and services to enable a great shopping experience. You will go home and show your family and friends why they receive this ad on search or social channels or that email from Amazon. You will make a difference by improving the relevancy for customers and optimizing the investment level for Amazon. State-of-the-art technology and algorithms including econometric methods, statistical modeling, machine learning, and data mining are the core of our business. Marketing drives a large portion of Amazon’s traffic and business, and represents a unique opportunity to drive impact on the company’s bottom line. We also focus on developing novel A/B experimentation mechanisms to measure efficacy of our ML solutions. With essentially full ownership of our own product roadmap, there is a large R&D component to our work, and strong engineering skills together with sound business understanding and an appetite for innovation are highly valued.

We are seeking a self-directed Senior Applied Scientist to develop state of the art machine learning algorithms including generative AI applications for large scale bidding and LLM aided marketing content generation to power Amazon ads. With over a billion product offers and ads worldwide, our programs are some of the largest across Amazon. This role operates as a subject-matter expert on statistical and scientific methods across traffic channels (Search, Social, Email, Push, and Creators), and will work directly with our advertising partners (e.g., Meta, Google, Microsoft, Tiktok, Pinterest, adMarketplace etc), market intelligence tools and internal stakeholder teams to solve challenging scientific problems.


Key job responsibilities
- Design, implement, test, deploy, and maintain innovative data and machine learning solutions, including state-of-the-art real time bidding and optimization models using advanced machine learning techniques.
- Provide inputs to the product roadmap, emphasizing research and development (R&D) to continuously enhance bidding capabilities.
- Model development, validation and deployment using Internal Amazon tools and public services such as AWS SageMaker for large-scale applications.
- Collaborate with scientists, engineers, product managers, and business stakeholders to design and implement software solutions for science problems.
- Superior verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts.

A day in the life
As a Senior Applied scientist on our team, you will leverage your strong background in Computer Science and Machine Learning to help build the next generation of our model development and assessment pipeline, harness and explain rich data at Amazon scale, and provide automated insights to improve machine learned solutions that impact millions of customers every day. This role requires a pragmatic technical leader comfortable with ambiguity, capable of summarizing complex data and models through clear visual and written explanations. The ideal candidate will have experience with machine learning models and information retrieval system. We are particularly interested in experience in building large scale marketing spend optimization models.

About the team
We are a team of scientists with engineering expertise. We work on prediction, optimization, and experimentation problems to provide data-driven inputs to marketing decisions and build highly scalable machine learning models across Automated Marketing and Events (AME) org to drive long-term profitability. Specifically, the team focuses on building re-usable science solutions to address three focal areas: (i) Content selection, creation and moderation, (ii) Bidding which involves valuation,
efficiency management and net-profit maximization via elasticity measurement, and (iii) Scalable Experimentation frameworks and statistical techniques for designing and performing causal analysis.