Principal Applied Scientist, Trusted Supply, Amazon Ads in London

Amazon Advertising is a fast-growing multi-billion dollar business that spans desktop, mobile, and connected devices; encompasses ads on Amazon and a vast network of hundreds of thousands of third-party publishers; and extends across US, EU, and an expanding number of international geographies. The Trusted Supply organization has the charter to safeguard advertiser trust and ensure high-quality ad impressions across all Amazon Advertising surfaces. We develop advanced algorithms and infrastructure systems to protect advertisers from unsafe content adjacency, low-quality inventory, fraud and privacy threats. Our scope spans a wide variety of problems in computational advertising including brand safety classification, content suitability scoring, risk hunting and proactive threat detection, viewability prediction, Made-for-Advertising (MFA) detection, malvertising identification, and privacy-preserving measurement and integration.

We are looking for an exceptional Principal Applied Scientist to define and drive the science vision across Brand Safety, Suitability, and Risk Hunting as primary areas of focus, while contributing to broader Supply Quality challenges around viewability, privacy-preserving solutions, and data leakage prevention. This is a high-visibility leadership role where your models and systems will process billions of ad impressions daily, directly impacting advertiser confidence, customer experience, and a multi-billion dollar business.

Key job responsibilities

• Set the science vision — defining multi-year research directions, establishing the publication roadmap, and driving innovations.
• Operate across programs — influence modeling frameworks across brand safety, MFA detection, traffic quality, viewability, and 3P integrations; break down silos between science and engineering teams.
• Act as a thought leader — anticipate industry shifts (privacy regulations, adversarial evolution, GenAI-powered threats), propose counter-strategies before they become critical, and represent Amazon in industry forums (TAG, MRC, IAB).
• Hire, mentor, and grow a high-performing team of applied scientists and research engineers; establish a culture of scientific rigor, peer-reviewed publications, and rapid experimentation.
• Partner with engineering leaders to build efficient, scalable, low-latency production systems that serve models at billions-of-requests-per-day scale.
• Influence product and business strategy — translate science capabilities into advertiser-facing products (targeting controls, transparency reports, quality guarantees) and quantify business impact.

Basic Qualifications

• Ph.D. in Computer Science, Machine Learning, Statistics, or a highly quantitative field.
• Experience applying machine learning to real-world problems at scale, with multiple years in a science leadership capacity.
• Proven track record of leading, mentoring, and growing teams of scientists (5+ scientists).
• Deep expertise in NLP, Computer Vision, or multi-modal learning with demonstrated impact in production systems.
• Strong publication record in top-tier ML/AI conferences (NeurIPS, ICML, KDD, WWW, ACL, EMNLP, CVPR, or equivalent).
• Experience with large-scale distributed ML systems processing terabytes of data.
• Expert-level proficiency in Python and at least one systems language (Java, C++, Scala).
• Demonstrated ability to translate ambiguous business problems into well-defined science initiatives with measurable outcomes.

Preferred Qualifications

• Experience with GenAI/LLM-based classification systems at production scale.
• Experience in computational advertising, ad tech, content moderation, trust & safety, or fraud/abuse detection.
• Expertise in adversarial machine learning, anomaly detection, or security-oriented ML applications.
• Familiarity with industry standards: MRC accreditation, TAG certification, brand safety frameworks, IAB content taxonomy.
• Experience with privacy-preserving ML techniques (federated learning, differential privacy, on-device inference).
• Track record of defining org-level research practices and shipping 0-to-1 science products.
• Experience with real-time inference systems operating at low latency.