About the job
Amazon is hiring a Senior Applied Scientist within Customer Forecasting and Valuation (CFV). CFV owns several of the primary decision metrics Amazon uses to evaluate launches and investments — causal estimates of how customer actions today translate into customer value over the year ahead. These metrics are how Amazon works backwards from the customer at scale: they let thousands of launch decisions a year, across Retail, Ads, Marketing, and Selection, weigh short-term profitability against long-term growth.
We are in the middle of a generational rebuild of how these metrics are produced. Our team is developing transformer-based foundation models of customer behavior, learned directly from billions of behavioral events. They are being built as shared infrastructure: one learned representation of customer behavior that a wide range of measurement and optimization systems across Amazon can be built on top of.
CFV is part of the Customer Behavior Analytics (CBA) organization, which builds the tools used to understand customer behavior and value generation across Amazon's Retail business.
Responsibilities
- Work at the intersection of causal inference, sequence modeling, and experimentation
- Develop transformer-based foundation models of customer behavior learned directly from billions of behavioral events
- Make learned representations estimation-aware and close the loop between experimental ground truth and model training
- Extrapolate short-horizon observations into year-ahead causal effects
- Translate methodological inventions into production systems that move real launch decisions
- Partner with other senior scientists, product owners, business leaders, and a dedicated engineering team
Qualifications
Minimum
- 4+ years of applied research experience
- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
Preferred
- Have peer-reviewed scientific contributions in premier journals and conferences