Senior Applied Scientist, Traffic Quality

Amazon Ads is a multi-billion dollar global business that delivers advertising experiences across Amazons owned-and-operated properties (including Prime Video Twitch Fire TV and ) third-party publisher networks and emerging channels like generative AI-powered shopping experiences. As one of the fastest-growing segments of Amazon we operate at unprecedented scale across desktop mobile connected TV and emerging surfaces.

Within Amazon Ads Traffic Quality is a critical pillar of advertiser trust and marketplace integrity. Our mission is to build advanced capabilities that work at petabyte scale to detect sophisticated invalid traffic (IVT) which includes sophisticated non-human traffic bot networks and fraudulent engagement patterns across programmatic advertising. We are on a journey to establish Amazon Ads as an industry leader in traffic quality standards and transparency. Our research agenda focuses on staying ahead of adversarial actors through continuous innovation in detection methodologies leveraging state-of-the-art techniques in deep learning and generative modeling user behavior and multi-modal representation learning anomaly detection time-series analysis and sparse labeling methods. We process billions of ad events daily developing novel algorithms that balance precision and recall while operating under strict latency constraints. Our work directly protects hundreds of millions of dollars in advertiser spend annually while maintaining a seamless user experience.

Key job responsibilities
Strategic Leadership & Vision
- Define long-term science vision for Traffic Quality driven by advertiser and publisher needs translating direction into actionable team plans.
- Solve strategically important business problems independently delivering robust scalable scientific solutions with limited guidance.
- Proactively identify technology gaps and business opportunities determining resource allocation priorities.

Scientific Innovation & Execution
- Design and implement statistical and machine learning solutions to detect robotic and human traffic patterns across billions of daily ad events.
- Own full development cycle for production-level code handling billions of ad requests: design prototype A/B testing and deployment.
- Stay current with scientific advancements and build publication strategy while championing excellence best practices.
- Directly protect hundreds of millions of dollars in advertiser spend annually while maintaining seamless user experience.
- Partner with engineers product managers and cross-functional teams to solve complex IVT detection problems and influence strategic initiatives.
- Mentor scientists on the team.

About the team
Here are a few papers published by the team:
1/ Scaling Generative Pre-training for User Ad Activity Sequences. AdKDD 2023.( SLIDR: Real-time Robot Detection On Online Ads IAAI 2023 Deployed Highly Innovative Applications of AI Track (AAAI 2023)( Self-supervised Representation Learning Across Sequential and Tabular Features Using Transformers NeurIPS 2022 First Table Representation Learning Workshop( 3 years of building machine learning models for business application experience
- PhD or Masters 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

- Experience with modeling tools such as R scikit-learn Spark MLLib MxNet Tensorflow numpy scipy etc.
- Experience with large scale distributed systems such as Hadoop Spark etc.

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