Applied Scientist - Generative AI for Finance Science

Amazon; Seattle, Northern (WA, KY); Hybrid; USD 136,000 - 184,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Job summary

Amazon seeks an Applied Scientist for the Stores Finance Science team in Seattle to advance ML-based financial processes and insights across the organization. You will design models that improve controllership, planning, and operational efficiency, and help translate complex data into actionable recommendations.

Collaborating with finance partners, software engineers, and data engineers, you will scale solutions using state‑of‑the‑art language models and deploy Gen AI capabilities in a

Qualifications

• Experience programming in Java, C++, Python or related language
• Experience with SQL and an RDBMS or Data Warehouse
• Master's degree or above in Engineering, Computer Science, Machine Learning, Operations Research, Statistics, or related fields
• Experience building machine learning models or developing algorithms for business application

Responsibilities

• Develop approaches to automate and enhance financial processes using state-of-the-art language models
• Partner with business stakeholders to identify high-impact opportunities for AI-driven transformation
• Work with Software Engineering and Data Engineering to implement solutions that scale to the entire organization

Skills

Programming in Java/C++/Python

SQL & Data Warehouse

ML model development

Education

Master's degree or above in Engineering/CS/ML/OR/Statistics

Tools

LLMs in production on GPUs/TPU

Job description

Amazon seeks an Applied Scientist for the Stores Finance Science team in Seattle to advance ML-based financial processes and insights across the organization. You will design models that improve controllership, planning, and operational efficiency, and help translate complex data into actionable recommendations.

Collaborating with finance partners, software engineers, and data engineers, you will scale solutions using state‑of‑the‑art language models and deploy Gen AI capabilities in a