Senior Applied Scientist, Traffic Quality

Amazon; Karnataka; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

Amazon is seeking a Senior Applied Scientist for Traffic Quality to build and deploy scalable ML models that detect fraudulent traffic across billions of ad events.

You will own end-to-end development, partner with cross-functional teams, and mentor junior scientists while advancing state-of-the-art techniques in deep learning and multimodal representations.

Qualifications

• 3+ years of building machine learning models for business applications.
• PhD or Master’s degree with 6+ years of applied research experience.
• Experience programming in Java, C++, Python or related language.
• Experience with neural deep learning methods and ML.

Responsibilities

• Design and implement ML solutions to detect traffic patterns at scale.
• Own end-to-end development for production-grade code handling billions of requests.
• Collaborate with engineers and product teams to solve IVT detection problems.
• Mentor scientists and drive publication and best-practices.

Skills

Machine learning

Programming

Python

Java

C++

Education

PhD or Master's +6 years research

Tools

TensorFlow

Scikit-learn

Spark

MxNet

Hadoop

Job description

Senior Applied Scientist, Traffic Quality

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

Strategic Leadership & Vision

• 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.

Scientific Innovation & Execution

• 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.

About 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 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

Preferred Qualifications

• 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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Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.