Senior Applied Scientist, Traffic Quality
Core
Define long-term science vision and build advanced capabilities to detect sophisticated invalid traffic (IVT), including non-human traffic and bot networks, across programmatic advertising at petabyte scale.
Role type
Senior Applied Scientist (Traffic Quality)
Builds
Production-level ML solutions and detection algorithms for invalid traffic
Domain
Advertising technology, fraud detection, deep learning
Deliverable
production ML models
Required skills
strategic leadership, statistical modeling, machine learning, deep learning, neural networks, anomaly detection, time-series analysis, sparse labeling, algorithm design, A/B testing, code deployment, mentorship
Preferred skills
R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy, large scale distributed systems, Hadoop
Technologies
Java, C++, Python, R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy, Hadoop, Spark
Responsibilities
Define long-term science vision for Traffic Quality; Design and implement statistical and machine learning solutions to detect robotic and human traffic patterns; Own full development cycle for production-level code handling billions of ad requests; Mentor scientists on the team
Seniority
Senior, hands-on IC with strategic leadership
