Senior ML Scientist, Sponsored Ads Response Optimization

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

Benefits offered by this job

Health insurance

RSUs

401(k) matching

Paid time off

Job summary

Amazon Ads is seeking a Sr Applied Scientist to advance Sponsored Products and Brands response prediction with deep learning and scalable ML systems. You will partner with engineers to deploy end-to-end solutions, run A/B tests, and derive insights from large data sets.

The role emphasizes responsible AI, cross‑functional collaboration, and building state-of-the-art models that improve ad relevance and performance across Amazon surfaces.

Qualifications

• 3+ years of building ML models for business applications.
• PhD or Master’s with 6+ years of applied research experience.
• Proficiency in Java, C++, Python; strong knowledge of deep learning concepts.

Responsibilities

• Conduct deep data analysis to derive insights for the business and identify opportunities.
• Develop scalable ML models and optimization strategies for business problems.
• Run regular A/B experiments, gather data, and perform statistical analysis.
• Collaborate with software engineers to deliver end-to-end production solutions.
• Improve scalability and automation of large-scale data analytics, model training, deployment and serving.
• Research new ML modeling approaches to optimize Sponsored Products and Brands.

Skills

Java

C++

Python

Deep learning

Distributed systems

A/B testing

Education

PhD or MS + 6+ years of applied research

Tools

R

scikit-learn

Spark MLLib

TensorFlow

MXNet

Hadoop

Spark

Job description

Amazon Ads is seeking a Sr Applied Scientist to advance Sponsored Products and Brands response prediction with deep learning and scalable ML systems. You will partner with engineers to deploy end-to-end solutions, run A/B tests, and derive insights from large data sets.

The role emphasizes responsible AI, cross‑functional collaboration, and building state-of-the-art models that improve ad relevance and performance across Amazon surfaces.