Description

You will design and deploy production-quality machine learning models to detect sophisticated invalid traffic and fraud, directly impacting advertiser trust and business outcomes.

This role is on-site.

Responsibilities

• Invent and adapt new machine learning approaches, models, and algorithms for fraud detection where problems are ill-defined.
• Deploy production-quality ML components that scale to process petabyte-scale data and billions of events daily.
• Perform broad data analysis on unstructured datasets to build business insights and drive modeling strategies.
• Produce research reports meeting top-tier external publication standards and contribute to the scientific community through peer-reviewed venues.

Required Skills

• 5+ years of experience in Deep Learning, Machine Learning, and Python.
• Strong background in Fraud Detection, Data Analysis, and Anomaly Detection.
• Experience with Self-Supervised Learning, Representation Learning, Clustering, and Time Series Analysis.
• Proven ability to work with unstructured and massive datasets at petabyte scale.

Preferred Skills

• Experience with Amazon cloud services (EC2, S3, EMR, Sagemaker, RedShift).
• Knowledge of generative modeling, multi-modal representation learning, and sparse labeling methods.
• Background in advertising fraud detection and invalid traffic detection systems.

Key Skills

Education

Any Graduate