Applied Scientist, Traffic Quality
Amazon· Applied Science
About this role
Amazon Ads is a multi-billion dollar global business that delivers advertising experiences across Amazon's owned-and-operated properties (including Prime Video, Twitch, Fire TV, and Amazon.com), third-party publisher networks, and emerging channels like generative AI-powered shopping experiences. As one of the fastest-growing segments of Amazon, we operate at unprecedented scale across desktop, mobile, connected TV, and emerging surfaces.
Within Amazon Ads, Traffic Quality is a critical pillar of advertiser trust and marketplace integrity. Our mission is to build advanced capabilities that work at petabyte scale to detect sophisticated invalid traffic (IVT) which includes sophisticated non-human traffic, bot networks, and fraudulent engagement patterns across programmatic advertising. We are on a journey to establish Amazon Ads as an industry leader in traffic quality standards and transparency. Our research agenda focuses on staying ahead of adversarial actors through continuous innovation in detection methodologies, leveraging state-of-the-art techniques in deep learning and generative modeling, user behavior and multi-modal representation learning, anomaly detection, time-series analysis, and sparse labeling methods. We process billions of ad events daily, developing novel algorithms that balance precision and recall while operating under strict latency constraints. Our work directly protects hundreds of millions of dollars in advertiser spend annually while maintaining a seamless user experience.
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)
Within Amazon Ads, Traffic Quality is a critical pillar of advertiser trust and marketplace integrity. Our mission is to build advanced capabilities that work at petabyte scale to detect sophisticated invalid traffic (IVT) which includes sophisticated non-human traffic, bot networks, and fraudulent engagement patterns across programmatic advertising. We are on a journey to establish Amazon Ads as an industry leader in traffic quality standards and transparency. Our research agenda focuses on staying ahead of adversarial actors through continuous innovation in detection methodologies, leveraging state-of-the-art techniques in deep learning and generative modeling, user behavior and multi-modal representation learning, anomaly detection, time-series analysis, and sparse labeling methods. We process billions of ad events daily, developing novel algorithms that balance precision and recall while operating under strict latency constraints. Our work directly protects hundreds of millions of dollars in advertiser spend annually while maintaining a seamless user experience.
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)
Basic qualifications
- 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
Preferred qualifications
- 2+ years of hands-on predictive modeling and large data analysis experience
- 2+ years of designing experiments and statistical analysis of results experience
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
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Frequently Asked Questions
Is the salary disclosed for the Applied Scientist, Traffic Quality position at Amazon?
The salary for this Applied Scientist, Traffic Quality role at Amazon is not publicly listed. Click "Apply Now" to learn more about the compensation package on their official careers page.
Where is the Applied Scientist, Traffic Quality position at Amazon located?
This Applied Scientist, Traffic Quality role at Amazon is based in Bengaluru, Karnataka, IND. The position is listed as on-site or hybrid. Check the full job description or apply directly to confirm the work arrangement.
Is the Applied Scientist, Traffic Quality role at Amazon full-time or part-time?
This is listed as a full time position. It is posted as a Applied Scientist, Traffic Quality role in the Applied Science department at Amazon.
Which team or department does the Applied Scientist, Traffic Quality at Amazon belong to?
This Applied Scientist, Traffic Quality position is part of the Applied Science department at Amazon. See the full job description for more information about the team structure and responsibilities.
How do I apply for the Applied Scientist, Traffic Quality position at Amazon?
Click the "Apply Now" button on this page. You will be redirected to Amazon's official application portal hosted on amazonjobs where you can submit your application directly.
When was the Applied Scientist, Traffic Quality job at Amazon posted?
This Applied Scientist, Traffic Quality position at Amazon was posted on Sep 14, 2026. Apply as soon as possible — early applications are often reviewed first.
Applied Scientist, Traffic Quality
Amazon
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