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. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day!
Our team owns personalization, guidance and experimentation science and is placed centrally in the organization which owns the Amazon Advertising Console from UX design to components and services. We leverage a breadth of state-of-the-art techniques such as deep learning, reinforcement learning, LLMs, long term causal modeling as well as sophisticated A/B testing to develop personalized experiences with directly quantifiable business and advertiser success outcomes. We are looking for an accomplished machine learning expert to lead the Applied Science strategy for our recommendation creation and recommendation personalziation program.
In this role, you will work closely with business leaders, stakeholders and cross-functional teams to drive program success through ML-driven solutions. You will shape the applied science roadmap, promote a culture of data-driven decision-making, and deliver significant business impact for millions of advertisers worldwide and the company using advanced data techniques and applied science methodologies.
Key job responsibilities
As an Applied Scientist on this team, you will
- Serve as the technical leader in Machine Learning, driving collaboration between scientists and engineers, guiding efforts within the team and collaborating with other teams.
- Conduct hands-on analysis and modeling of large-scale data to generate insights that boost ads revenue while maintaining a positive advertiser and shopper experience.
- Lead end-to-end Machine Learning projects that involve high levels of ambiguity, scale, and complexity.
- Build, experiment, optimize, and deploy machine learning models, collaborating with software engineers to bring your models into production.
- Run A/B experiments, gather data, and perform statistical analysis to validate your models.
- Develop scalable and automated processes for large-scale data analysis, model development, validation, and serving.
- Explore and research innovative machine learning approaches to push the boundaries of what’s possible.
