Applied Scientist, Gen AI for Finance & Controllership

Amazon.com Services LLC; Bellevue (WA); On-site; USD 150,000 - 230,000; Full time

Benefits offered by this job

Health insurance

Restricted Stock Units (RSUs)

Job summary

Amazon Stores Finance Science (ASFS) is hiring an Applied Scientist to accelerate Gen AI within Finance. The role focuses on leveraging large language models and agentic AI to improve decision-making, automate controllership processes, and boost operational efficiency across Stores Finance.

Responsibilities include automating financial processes with state-of-the-art LLMs, partnering with stakeholders to identify high-impact AI opportunities, and delivering scalable solutions with SDE/Data Eng

Qualifications

• Experience programming in Java, C++, Python or related language.
• Experience with SQL and an RDBMS (e.g., Oracle) 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

Java

C++

Python

SQL

Machine Learning

Education

Master's degree

Tools

Oracle

Job description

Amazon Stores Finance Science (ASFS) is hiring an Applied Scientist to accelerate Gen AI within Finance. The role focuses on leveraging large language models and agentic AI to improve decision-making, automate controllership processes, and boost operational efficiency across Stores Finance.

Responsibilities include automating financial processes with state-of-the-art LLMs, partnering with stakeholders to identify high-impact AI opportunities, and delivering scalable solutions with SDE/Data Eng