Senior Applied Scientist, GenAI for Ads Response Prediction

Amazon Science; Palo Alto (CA); On-site; USD 192,000 - 260,000; Full time

Job summary

Amazon Ads is seeking a Machine Learning Applied Scientist to drive data-driven decision making for Sponsored Products and Brands. You will build scalable ML models, run A/B tests, and collaborate with software engineers to deploy end-to-end solutions across ads platforms worldwide.

You will explore generative AI techniques to optimize ad serving, prediction, and targeting, while advancing model training and deployment at scale.

Qualifications

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

Responsibilities

• Conduct deep data analysis to derive insights to the business, and identify gaps and new opportunities
• Develop scalable and effective machine-learning models and optimization strategies to solve business problems
• Run regular A/B experiments, gather data, and perform statistical analysis
• Work closely with software engineers to deliver end-to-end solutions into production
• Improve the scalability, efficiency and automation of large-scale data analytics, model training, deployment and serving
• Conduct research on new machine-learning modeling and GenAI solutions to optimize all aspects of Sponsored Products and Brands business

Skills

Machine learning

Python

Java/C++

Deep learning

Education

PhD or Master's + 6+ years applied research

Tools

TensorFlow

Scikit-learn

Spark MLLib

Hadoop

Job description

Amazon Ads is seeking a Machine Learning Applied Scientist to drive data-driven decision making for Sponsored Products and Brands. You will build scalable ML models, run A/B tests, and collaborate with software engineers to deploy end-to-end solutions across ads platforms worldwide.

You will explore generative AI techniques to optimize ad serving, prediction, and targeting, while advancing model training and deployment at scale.