Data Scientist, Traffic Quality

Amazon Amazon · Big Tech · IN, KA, Bengaluru · Research Science

The Data Scientist II in Traffic Quality at Amazon Ads will focus on applying advanced statistical techniques and machine learning to detect sophisticated invalid traffic (IVT) in advertising. This role involves defining new research problems, applying ML models to detect fraud, performing data analysis, working with massive datasets, and producing research reports. The team leverages deep learning, generative modeling, user behavior analysis, anomaly detection, and time-series analysis to protect advertiser spend and maintain marketplace integrity. The role also involves mentoring junior scientists and has a strong publication record.

What you'd actually do

  1. Define and frame new research problems in fraud detection where neither problem nor solution is well-defined.
  2. Apply new machine learning approaches, models, and algorithms to detect sophisticated invalid traffic.
  3. Apply domain knowledge to perform broad data analysis as a precursor to modeling and build business insights.
  4. Work with unstructured and massive datasets to deliver results.
  5. Produce research reports meeting top-tier external publication standards.

Skills

Required

  • 2+ years of data scientist experience
  • 3+ 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
  • 1+ years of guiding and coaching a group of researchers experience
  • 1+ years of working with or evaluating AI systems experience
  • 1+ years of creating or contributing to mathematical textbooks, research papers, or educational content experience
  • Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM)
  • Experience applying theoretical models in an applied environment

Nice to have

  • Knowledge of machine learning concepts and their application to reasoning and problem-solving
  • Experience in Python, Perl, or another scripting language
  • Experience in a ML or data scientist role with a large technology company
  • Experience in defining and creating benchmarks for assessing GenAI model performance
  • Experience working on multi-team, cross-disciplinary projects
  • Experience applying quantitative analysis to solve business problems and making data-driven business decisions
  • Experience effectively communicating complex concepts through written and verbal communication

What the JD emphasized

  • Define and frame new research problems in fraud detection where neither problem nor solution is well-defined.
  • Produce research reports meeting top-tier external publication standards.

Other signals

  • detect sophisticated invalid traffic (IVT)
  • leveraging state-of-the-art techniques in deep learning and generative modeling
  • user behavior and multi-modal representation learning
  • anomaly detection, time-series analysis
  • sparse labeling methods
  • billions of ad events daily
  • novel algorithms that balance precision and recall
  • strict latency constraints