Senior Applied Scientist, Traffic Quality

Here are a few papers published by the team:

1/ [Scaling Generative Pre-training for User Ad Activity Sequences. AdKDD 2023.](https://assets.amazon.science/b7/42/03be071743d5a57cb1656e6caa34/scaling-generative-pre-training-for-user-ad-activity-sequences.pdf)

2/ [SLIDR: Real-time Robot Detection On Online Ads, IAAI 2023, Deployed Highly Innovative Applications of AI Track (AAAI 2023)](https://assets.amazon.science/75/2f/3b7106b143f38f7f4d2806388ace/real-time-detection-of-robotic-traffic-in-online-advertising.pdf)

3/ [Self-supervised Representation Learning Across Sequential and Tabular Features Using Transformers, NeurIPS 2022, First Table Representation Learning Workshop](https://openreview.net/forum?id=wIIJlmr1Dsk)

• 3+ years of building machine learning models for business application experience
• PhD, or Master's degree and 6+ years of applied research experience
• Experience programming in Java, C++, Python or related language
• Experience with neural deep learning methods and machine learning
• Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
• Experience with large scale distributed systems such as Hadoop, Spark etc.

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• Define long-term science vision for Traffic Quality driven by advertiser and publisher needs, translating direction into actionable team plans.
• Solve strategically important business problems independently, delivering robust, scalable scientific solutions with limited guidance.
• Proactively identify technology gaps and business opportunities, determining resource allocation priorities.

• Design and implement statistical and machine learning solutions to detect robotic and human traffic patterns across billions of daily ad events.
• Own full development cycle for production-level code handling billions of ad requests: design, prototype, A/B testing, and deployment.
• Stay current with scientific advancements and build publication strategy while championing excellence best practices.
• Directly protect hundreds of millions of dollars in advertiser spend annually while maintaining seamless user experience.
• Partner with engineers, product managers, and cross-functional teams to solve complex IVT detection problems and influence strategic initiatives.
• Mentor scientists on the team.