Applied Scientist, Ad Fraud Detection
1 week ago
MS/PhD in Machine Learning, Statistics, Optimization or Applied Mathematics
- Relevant industry or research experience
- Hands-on experience in predictive modeling and analysis
- Strong skills in problem solving, programming and computer science fundamentals
Advertising at Amazon is a fast-growing multi-billion dollar business that spans across 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 increasing number of international geographies. One of the key focus areas is Traffic Quality where we endeavour to identify non-human and invalid traffic within programmatic ad sources, and weed them out to ensure a high quality advertising marketplace. We do this by building machine learning and optimization algorithms that operate at scale, and leverage nuanced features about user, context, and creative engagement to determine the validity of traffic. The challenge is to stay one step ahead by investing in deep analytics and developing new algorithms that address emergent attack vectors in a structured and scalable fashion. We are committed to building a long-term traffic quality solution that encompasses all Amazon advertising channels and provides state-of-the-art traffic filtering that preserves advertiser trust and saves them hundreds of millions of dollars of wasted spend.
Bangalore, KA, IND
- Experience with Big Data
- Experience in Deep Learning
- Experience in Computational Advertising
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