Senior Applied Science Manager, 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.
- Lead teams solving 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.
- Hold team to highest scientific standards reviewing modeling decisions and evaluating proposals for strengths and weaknesses.
- Make sophisticated trade-offs balancing precision and recall under strict latency constraints.
- Scope projects design experiments and improve methodologies for new data sources and model enhancements.
- Stay current with scientific advancements and build publication strategy while championing excellence best practices.

Operational Excellence & Customer Trust
- Maintain advertiser trust through near real-time monitoring systems responding rapidly to anomalies and metric deviations.
- Ensure operational excellence through proactive quality signal investigation root cause analysis and swift remediation.
- Directly protect hundreds of millions of dollars in advertiser spend annually while maintaining seamless user experience.

Collaboration & Team Development
- Partner with engineers product managers and cross-functional teams to solve complex IVT detection problems and influence strategic initiatives.
- Hire manage coach and promote scientists while building succession plans and growing future leaders.
- Structure teams sustainably to meet scientific business and technology needs while fostering innovation culture.

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( 10 years of building large-scale machine learning and AI solutions at Internet scale experience
- Masters degree in Computer Science (Machine Learning AI Statistics or equivalent)
- Experience distilling informal customer requirements into problem definitions dealing with ambiguity and competing objectives
- Experience hiring and leading experienced scientists as well as having a successful record of developing junior members from academia or industry to a successful career track
- 5 years of people management experience

- 10 years of practical work applying ML to solve complex problems for large-scale applications experience
- 5 years of hands-on work in big data machine learning and predictive modeling experience
- PhD in Computer Science (Machine Learning AI Statistics or equivalent)
- Experience working with big data machine learning and predictive modeling
- Experience with big data technologies such as AWS Hadoop Spark Pig Hive etc.
- Experience with Java C or other programming language as well as with R MATLAB Python or an equivalent scripting language

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