Applied Scientist, Traffic Quality

Amazon

United States
On-site
Deep learning
Self-supervised techniques
Representation learning
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

Job Summary

  • 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.
  • You will solve inherently hard problems in advertising fraud detection using deep learning, self-supervised techniques, representation learning, and advanced clustering.
  • Produce research reports meeting top-tier external publication standards and contribute to the scientific community through publications at peer-reviewed venues.

Matching Summary

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.

Skills & Requirements

Must-have

  • deep learning
  • self-supervised techniques
  • representation learning
  • anomaly detection
  • time-series analysis

Nice-to-have

  • generative modeling
  • user behavior analysis
  • multi-modal representation learning
  • sparse labeling methods

Key Requirements

  • PhD or Master's degree in a quantitative field
  • Experience with deep learning frameworks
  • Experience with large-scale data processing
  • Experience in fraud detection or related areas

Work Rights

Not specified

Tailored Resume

Cover Letter