Key job responsibilities
As an Applied Scientist II in Traffic Quality, you will solve inherently hard problems in advertising fraud detection using deep learning, self-supervised techniques, representation learning, and advanced clustering. You'll work on systems that process billions of ad impressions and clicks per day, using Amazon's cloud services including EC2, S3, EMR, Sagemaker, and RedShift.
Define and frame new research problems in fraud detection where neither problem nor solution is well-defined.
Invent and adapt new machine learning approaches, models, and algorithms to detect sophisticated invalid traffic.
Design and deploy production-quality ML components that directly impact advertiser trust and the business top-line.
Apply domain knowledge to perform broad data analysis as a precursor to modeling and build business insights.
Work with unstructured and massive datasets to deliver results.
Produce research reports meeting top-tier external publication standards.
Contribute to the scientific community through publications at peer-reviewed venues and reviewing research submissions.
Mentor and develop junior scientists on the team.
About the team
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 models for business application experience
PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
Experience in patents or publications at top-tier peer-reviewed conferences or journals
Experience in solving business problems through machine learning, data mining and statistical algorithms
Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
2+ years of hands-on predictive modeling and large data analysis experience
2+ years of designing experiments and statistical analysis of results experience
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