Data Scientist, Ad Fraud Detection, Traffic Quality ML

- 3+ years of data scientist experience
- 4+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
- Experience applying theoretical models in an applied environment
- Experience with data scripting languages (e.g. SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)
- Experience with big data: processing, filtering, and presenting large quantities (100K to Millions of rows) of data
- Experience in a ML or data scientist role with a large technology company
- Experience in Computational Advertising

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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.

A Data Scientist is responsible for delivering deep data-driven analyses with insights that drive the business. They would use a combination of analytics, data visualization and machine learning to identify gaps in current solutions as well as prototype new algorithms that close the gaps. The ideal candidate should have strong experience in dive deep analytics and data visualization, thorough knowledge of statistical techniques and strong breadth in machine learning. The candidate should also have good programming and design skills to implement machine learnings algorithms in practice on massive unstructured datasets.