Senior Applied Scientist - ML for Catalog Expansion & Scale

Amazon Inc.; Seattle (WA); Hybrid; USD 167,000 - 226,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Amazon is seeking a Senior Applied Scientist to lead research in Shopping Convo Foundations - Pre-purchases Science. You will develop novel ML models to expand catalogue coverage, translate breakthroughs into production, and mentor junior scientists.

Based in Seattle, you will collaborate with ML Engineers and SDEs to optimize performance and deploy low-latency solutions at scale. The role requires strong expertise in AI/ML, experience leading teams, and a track record of solving complex

Qualifications

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

Responsibilities

• Lead the research and development of novel machine learning approaches to solve complex catalogue expansion and product attribute challenges
• Design and develop state-of-the-art ML models, conduct rigorous experimentation
• Translate scientific breakthroughs into production-ready solutions
• Guide a set of junior scientists
• Collaborate with ML Engineers and Software Development Engineers to optimize model performance, ensure scalability, and deploy low-latency solutions at Amazon scale

Skills

ML models

Java

C++

Python

Deep learning

Education

PhD or MS + 6+ years

Tools

R

scikit-learn

Spark MLLib

MxNet

TensorFlow

NumPy

SciPy

Job description

Amazon is seeking a Senior Applied Scientist to lead research in Shopping Convo Foundations - Pre-purchases Science. You will develop novel ML models to expand catalogue coverage, translate breakthroughs into production, and mentor junior scientists.

Based in Seattle, you will collaborate with ML Engineers and SDEs to optimize performance and deploy low-latency solutions at scale. The role requires strong expertise in AI/ML, experience leading teams, and a track record of solving complex